diff --git a/.claude/skills/compass-debug/SKILL.md b/.claude/skills/compass-debug/SKILL.md new file mode 100644 index 0000000..423fe39 --- /dev/null +++ b/.claude/skills/compass-debug/SKILL.md @@ -0,0 +1,140 @@ +--- +name: compass-debug +description: > + Diagnose why COMPASS isn't working: container, GPU, activated shell, + assets, Isaac Sim init, checkpoint validity. Use whenever the user + reports vague COMPASS errors — "training won't start", "something's + wrong", "why is this failing" — even without the word "debug". Make + sure to use this when the user mentions COMPASS isn't behaving and + doesn't have a more specific intent. +allowed-tools: + - Bash + - Read + - Grep + - Glob +--- + +You produce a diagnostic snapshot of a COMPASS dev environment and report what's working and what's broken. Read-only by design — you report problems but never auto-fix them. Once you've identified the root cause, point the user at the specialty skill that owns the fix. + +This skill is the place users land when training "just won't start" or something obscure breaks. The goal is to surface the actual root cause in one screen, not to chase the symptom. + +## When NOT to use this skill + +- The user knows what they want to do (train, deploy, etc.) and just needs the workflow → `/compass`, `/compass-deploy`, etc. +- The user wants to add a robot platform → `/compass-newembodiment`. +- A specific error message has a clear fix in the handbook → quote the relevant page; debug isn't needed. + +--- + +## Workflow + +### Step 1: Run the diagnostic script + +The skill bundles `scripts/compass_status.sh` which runs all checks in parallel and prints a markdown table: + +```bash +# dangerouslyDisableSandbox: true (script touches nvidia-smi) +bash /scripts/compass_status.sh +``` + +Output looks like: + +``` +| Status | Check | Detail | +|---|---|---| +| ✓ | Container | compass-rl up | +| ✓ | Activated shell | shim dir on PATH | +| ✓ | GPU | NVIDIA H100 80GB HBM3, 79980 MiB | +| ✓ | Base ckpt | ./assets/x_mobility.ckpt (1.2G) | +| ✓ | USDs | 8 entries in ./assets/usd/ | +| ✓ | Recent log | /tmp/isaaclab/logs/2026-05-08_14-23-02 | +``` + +For deeper diagnostics (slower, ~30s — runs Isaac Sim init headless to surface kit-init errors that don't show up in the quick check): + +```bash +bash /scripts/compass_status.sh --deep +``` + +For checkpoint-specific issues (verifies a `.pt` file is a valid torch checkpoint and not corrupted): + +```bash +bash /scripts/compass_status.sh --ckpt +``` + +### Step 2: Interpret the output + +The table is the report; just relay it to the user. Then add a one-paragraph interpretation: which row is the root cause, and what to do about it. + +| Failed check | Most likely cause | Where to fix | +|---|---|---| +| Container | Container hasn't been started | `./docker/run.sh up` (or `./docker/run.sh build` if image missing) | +| Activated shell | User invoked Claude in a fresh shell | `source ./docker/activate` then retry | +| GPU | Driver issue OR sandbox blocking nvidia-smi | Check `dangerouslyDisableSandbox: true` was set; if persists, check host driver with `nvidia-smi` from the host shell | +| Base ckpt / USDs | Assets not downloaded yet | `./docker/run.sh assets` (needs `HF_TOKEN`) | +| Recent log | No training has run yet (informational) | Not a failure — just confirms a clean state | +| Isaac Sim init | Container build broken OR GPU not exposed | Try `./docker/run.sh down && ./docker/run.sh build` | +| Ckpt load | File corrupt OR wrong torch version | Re-download / re-train; if re-train, route to `/compass` | + +### Step 3: Recommend the next skill + +Don't try to fix the issue yourself — different fixes belong to different skills: + +| Root cause class | Recommended next skill | +|---|---| +| Setup issue (container, assets, activated shell) | `/compass` (Setup COMPASS section) | +| Training-time issue (config, ckpt path, env name) | `/compass` (Train section) | +| Deploy-time issue (ONNX/TRT/ROS2) | `/compass-deploy` | +| New robot platform missing | `/compass-newembodiment` | + +Anti-pattern guard: don't run fixes yourself. The user benefits from understanding what went wrong; running fixes blind hides root causes. + +--- + +## Common patterns + +### "Training crashed silently" + +Look for the most-recent kit log: +```bash +find ~/.local/share/ov/pkg/isaac-sim-* -name "kit_*.log" 2>/dev/null | head -1 | xargs tail -100 +``` + +Common silent-crash causes: +- GPU OOM (look for "out of memory" / "CUDA error" near the tail). +- USD asset missing (look for "Failed to load" / file-path lines). +- Quaternion convention mismatch on a custom embodiment (look for "wxyz" / "xyzw"). + +### "Pre-flight passes but `python run.py …` still errors" + +Confirm the user is in an activated shell. The script checks PATH for the shim dir but a user can run scripts in any shell — verify with: +```bash +which python +# Should resolve to a /tmp/compass-shims.* path +``` + +If `which python` resolves to `/usr/bin/python` or similar, the shell isn't activated even if other env vars suggest it is. + +### "OSMO submission fails on `osmo workflow submit`" + +That's an OSMO-side issue, not a local one. Check: +```bash +test -n "${WANDB_API_KEY:-}" && echo "WANDB_API_KEY set" || echo "WANDB_API_KEY MISSING" +test -n "${HF_TOKEN:-}" && echo "HF_TOKEN set" || echo "HF_TOKEN MISSING" +test -n "${COMPASS_OSMO_REGISTRY:-}" && echo "registry set" || echo "registry MISSING (or pass --image)" +``` + +If all set, route the user to `/compass` (OSMO submission section in that skill body). + +--- + +## Key File Locations + +| File | Purpose | +|------|---------| +| `/scripts/compass_status.sh` | The diagnostic script; usable as a standalone CLI | +| `./docker/run.sh` | Container lifecycle (status / up / down / build / assets / shell) | +| `./docker/activate` | Shell activate script (sets up python/pip shims) | +| `./assets/` | Asset bind-mount (USDs + base ckpt) | +| `/tmp/isaaclab/logs/` | Training run logs (latest is most useful) | +| `~/.local/share/ov/pkg/isaac-sim-*/kit_*.log` | Kit logs for crash diagnostics | diff --git a/.claude/skills/compass-debug/scripts/compass_status.sh b/.claude/skills/compass-debug/scripts/compass_status.sh new file mode 100755 index 0000000..ab8e488 --- /dev/null +++ b/.claude/skills/compass-debug/scripts/compass_status.sh @@ -0,0 +1,138 @@ +#!/usr/bin/env bash +# compass_status.sh — diagnostic snapshot of a COMPASS dev environment. +# +# Usage: +# ./compass_status.sh # quick checks (~1s) +# ./compass_status.sh --deep # also runs Isaac Sim init test (~30s) +# ./compass_status.sh --ckpt PATH # also load a specific .pt file with torch +# +# Exit code: 0 if all required checks pass, 1 if any fail. +# WARN-level entries don't fail the run. + +set -uo pipefail + +DEEP=0 +CKPT_PATH="" +while [ "$#" -gt 0 ]; do + case "$1" in + --deep) DEEP=1; shift ;; + --ckpt) CKPT_PATH="${2:-}"; shift 2 ;; + -h|--help) + sed -n '4,9p' "$0" | sed 's/^# \?//' + exit 0 + ;; + *) echo "Unknown flag: $1" >&2; exit 2 ;; + esac +done + +# Resolve repo root (script lives at .claude/skills/compass-debug/scripts/). +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +REPO_ROOT="$(cd "${SCRIPT_DIR}/../../../.." && pwd)" +cd "${REPO_ROOT}" + +PASS="✓" +FAIL="✗" +WARN="⚠" + +declare -a TABLE +fail_count=0 + +row() { + local status="$1" name="$2" detail="$3" + TABLE+=("| ${status} | ${name} | ${detail} |") + [ "${status}" = "${FAIL}" ] && fail_count=$((fail_count + 1)) +} + +# 1. Container running? +if ./docker/run.sh status 2>/dev/null | grep -qE "Up|running"; then + row "${PASS}" "Container" "compass-rl up" +else + row "${FAIL}" "Container" "down — run \`./docker/run.sh up\`" +fi + +# 2. Activated shell? Detect by PATH containing the shim dir created by +# docker/activate (mktemp pattern: compass-shims.XXXXXX). +if echo "${PATH}" | grep -q "compass-shims\."; then + row "${PASS}" "Activated shell" "shim dir on PATH" +else + row "${WARN}" "Activated shell" "no — run \`source ./docker/activate\`" +fi + +# 3. GPU +if command -v nvidia-smi >/dev/null 2>&1; then + GPU_INFO="$(nvidia-smi --query-gpu=name,memory.free --format=csv,noheader 2>/dev/null | head -1)" + if [ -n "${GPU_INFO}" ]; then + row "${PASS}" "GPU" "${GPU_INFO}" + else + row "${FAIL}" "GPU" "nvidia-smi returned empty (driver issue?)" + fi +else + row "${FAIL}" "GPU" "nvidia-smi not on PATH" +fi + +# 4. Base policy ckpt +if [ -f "./assets/x_mobility.ckpt" ]; then + SIZE="$(du -h ./assets/x_mobility.ckpt | cut -f1)" + row "${PASS}" "Base ckpt" "./assets/x_mobility.ckpt (${SIZE})" +else + row "${FAIL}" "Base ckpt" "missing — run \`./docker/run.sh assets\`" +fi + +# 5. Built-in scene USDs +if [ -d "./assets/usd" ] && [ -n "$(ls -A ./assets/usd 2>/dev/null)" ]; then + USD_COUNT="$(ls ./assets/usd | wc -l)" + row "${PASS}" "USDs" "${USD_COUNT} entries in ./assets/usd/" +else + row "${FAIL}" "USDs" "missing — run \`./docker/run.sh assets\`" +fi + +# 6. Recent training log (informational; non-blocking) +LATEST="$(ls -t /tmp/isaaclab/logs/ 2>/dev/null | head -1 || true)" +if [ -n "${LATEST}" ]; then + row "${PASS}" "Recent log" "/tmp/isaaclab/logs/${LATEST}" +else + row "${WARN}" "Recent log" "none — no training has run yet on this machine" +fi + +# 7. Deep check: Isaac Sim init (only with --deep) +if [ "${DEEP}" = "1" ]; then + if python -c " +from isaacsim import SimulationApp +app = SimulationApp({'headless': True}) +app.close() +" >/dev/null 2>&1; then + row "${PASS}" "Isaac Sim init" "headless app start/close OK" + else + row "${FAIL}" "Isaac Sim init" "FAILED — check container build / GPU access" + fi +fi + +# 8. Optional: load a specific ckpt +if [ -n "${CKPT_PATH}" ]; then + if [ -f "${CKPT_PATH}" ] && python -c " +import torch +torch.load('${CKPT_PATH}', map_location='cpu') +" >/dev/null 2>&1; then + SIZE="$(du -h "${CKPT_PATH}" | cut -f1)" + row "${PASS}" "Ckpt load" "${CKPT_PATH} (${SIZE})" + else + row "${FAIL}" "Ckpt load" "${CKPT_PATH} (missing or not a valid torch ckpt)" + fi +fi + +# Print table +echo "| Status | Check | Detail |" +echo "|---|---|---|" +for line in "${TABLE[@]}"; do + echo "${line}" +done + +if [ "${fail_count}" -eq 0 ]; then + echo "" + echo "All checks passed." + exit 0 +else + echo "" + echo "${fail_count} check(s) failed. See https://nvlabs.github.io/COMPASS/docs/quickstart.html for setup." + exit 1 +fi diff --git a/.claude/skills/compass-deploy/SKILL.md b/.claude/skills/compass-deploy/SKILL.md new file mode 100644 index 0000000..2597596 --- /dev/null +++ b/.claude/skills/compass-deploy/SKILL.md @@ -0,0 +1,150 @@ +--- +name: compass-deploy +description: > + Deploy a TRAINED COMPASS checkpoint to a robot: ONNX export, TensorRT + engine build, ROS2 launch scaffold. Use whenever the user says deploy, + export to ONNX, TRT engine, run on a robot, sim2real, ROS2 deployment, + or has a checkpoint they want to ship. Make sure to use this even when + the user says things like "how do I get my policy on Carter?" without + the word "deploy". +allowed-tools: + - Bash + - Read + - Edit + - Write + - Grep + - Glob +--- + +You take a trained COMPASS policy from a checkpoint (`.pt`) all the way to a runnable robot launch — ONNX export, TensorRT engine build, then a ROS2 launch scaffold pointing at the engine. The user provides the checkpoint; you orchestrate the rest. + +This skill assumes the docker-as-venv environment from the `compass` skill — `source ./docker/activate` already done, container up, X-Mobility base policy at `./assets/x_mobility.ckpt`. If the user hasn't set that up, recommend `/compass` first. + +## When NOT to use this skill + +- The user is still training and hasn't picked a checkpoint yet → `/compass` (training flow). +- The user reports a deploy step is failing without a clear cause → run `/compass-debug` first to surface the root cause. +- The user wants to add a new robot platform (cfg files, embodiment registration) → `/compass-newembodiment`. + +--- + +## Prerequisites Check + +```bash +# Activated shell? +command -v deactivate >/dev/null && echo "shell: activated" || echo "NOT activated — run: source ./docker/activate" + +# Container running? +./docker/run.sh status + +# Base policy present? +test -f ./assets/x_mobility.ckpt && echo "x_mobility ckpt: OK" || echo "MISSING — run: ./docker/run.sh assets" + +# Trained checkpoint exists? (user supplies the path) +test -f "" && echo "ckpt: OK ($(du -h | cut -f1))" || echo "MISSING ckpt" +``` + +If anything fails, fix the upstream issue before continuing — pushing forward with a missing ckpt or unactivated shell wastes the user's time. + +--- + +## Workflow + +### Step 1: Pick the export branch + +The user's checkpoint is one of two kinds. Ask if it isn't obvious from filename or context: + +| Checkpoint type | Where it came from | ONNX export flags | +|---|---|---| +| **Specialist (residual)** | A `run.py` training run (per-embodiment) | `--residual-ckpt-path / -r` | +| **Generalist (distilled)** | A `distillation_train.py` run (multi-embodiment) | `--generalist-ckpt-path / -g` plus `--embodiment-type / -e ` | + +For a generalist, the `--embodiment-type` flag pins which embodiment's action head to export — pick from `carter`, `h1`, `spot`, `g1`, `digit`. Without it, ONNX export fails because the generalist has multiple action heads and only one can be exported per ONNX file. + +### Step 2: ONNX export + +Specialist: +```bash +python onnx_conversion.py \ + -b ./assets/x_mobility.ckpt \ + -r \ + -o /policy.onnx +``` + +Generalist: +```bash +python onnx_conversion.py \ + -b ./assets/x_mobility.ckpt \ + -g \ + -e \ + -o /policy.onnx +``` + +Optional flags worth surfacing if the user asks: +- `-j /policy.jit` — also export a TorchScript JIT for fallback / debug. +- `-a ` — upload the ONNX to W&B as a reusable artifact. +- `-i 224 224` — image size; defaults are usually fine. + +### Step 3: TensorRT engine build + +```bash +python trt_conversion.py \ + -o /policy.onnx \ + -t /policy.engine +``` + +The `.engine` file is the runtime artifact — that's what ROS2 loads. Engines are **GPU-architecture-specific**: an engine built on an H100 won't run on a Jetson. Build the engine on the same GPU class as the robot. + +### Step 4: ROS2 launch scaffold + +The reference launch file is `ros2_deployment/compass_navigator/launch/compass_navigator.launch.py`. It takes the engine via the `runtime_path` launch argument (default: `/tmp/compass_carter.engine`). + +For a real-robot bring-up: +```bash +# In a sourced ROS2 workspace +ros2 launch compass_navigator compass_navigator.launch.py \ + runtime_path:= +``` + +For Isaac Sim sim2real testing on the same machine: +```bash +# Terminal 1: bring up the Isaac Sim ROS2 bridge +# (separate process — don't run inside this skill; it competes for the GPU) +./ros2_deployment/launch_compass.sh sim + +# Terminal 2: launch the compass navigator +ros2 launch compass_navigator compass_navigator.launch.py \ + runtime_path:= +``` + +Per-platform specifics (Carter vs Spot vs Galileo, sensor remapping, NuRec real2sim setup) are documented in `docs/handbook/deployment/ros2.md`. Point the user there if their target platform isn't generic Carter. + +### Step 5: Smoke test (user-driven) + +Print the exact launch command for the user's platform and ask them to run it from a separate terminal — bringing up Isaac Sim or a real-robot stack inside this session is risky: +- Isaac Sim launches steal the GPU and block other operations. +- Real-robot bring-up needs hardware ssh / safety-monitor that isn't visible from here. +- Mistakes during deploy can drive a real robot into a wall. + +Anti-pattern guard: this skill **does not** invoke `ros2 launch` itself. It produces the engine file and the exact command; the user runs that command in a context where they can monitor and abort. + +--- + +## Common follow-ups + +- "It runs but the policy is wrong." Check the `--embodiment-type` flag matches the platform — easy to export with the wrong head. Re-export and rebuild. +- "TRT build fails with an op error." Usually means an Isaac Sim / TensorRT version mismatch. Ask the user which GPU the build is happening on; suggest re-running `./docker/run.sh build` if the image is stale. +- "Engine works in sim but not on the robot." GPU-arch mismatch — rebuild the engine on the robot's GPU class. + +For deeper diagnostics, route to `/compass-debug`. + +## Key File Locations + +| File | Purpose | +|------|---------| +| `onnx_conversion.py` | ONNX export entry point | +| `trt_conversion.py` | TRT engine build | +| `ros2_deployment/compass_navigator/launch/compass_navigator.launch.py` | Reference ROS2 launch file (takes `runtime_path:=`) | +| `ros2_deployment/launch_compass.sh` | Convenience launcher (sim / real / sim2real modes) | +| `docs/handbook/deployment/ros2.md` | Per-platform deployment matrix | +| `./assets/x_mobility.ckpt` | X-Mobility base policy (always required for ONNX export) | diff --git a/.claude/skills/compass-newembodiment/SKILL.md b/.claude/skills/compass-newembodiment/SKILL.md new file mode 100644 index 0000000..b322549 --- /dev/null +++ b/.claude/skills/compass-newembodiment/SKILL.md @@ -0,0 +1,138 @@ +--- +name: compass-newembodiment +description: > + Onboard a new robot platform to COMPASS: generate robot ArticulationCfg + + per-embodiment env_cfg, register in EmbodimentEnvCfgMap, smoke-test + the spawn with --num_envs 1. Use whenever the user wants to add a new + robot, register an embodiment, or extend COMPASS with their platform. + NOT for switching between built-in embodiments (use --embodiment) or + for adding a new scene (the `compass` skill covers SAGE → register). +allowed-tools: + - Bash + - Read + - Edit + - Write + - Grep + - Glob + - AskUserQuestion +--- + +You walk a user through onboarding a new robot platform to COMPASS — generating the right config files, registering the embodiment, and verifying the robot spawns correctly. The handbook's [`extending.md`](../../../docs/handbook/extending.md) is the canonical written guide; this skill is the operational sibling that does the work interactively. + +This skill assumes the docker-as-venv environment from the `compass` skill — `source ./docker/activate` already done, container up. If the user hasn't set that up, recommend `/compass` first. + +## When NOT to use this skill + +- The user wants to switch between built-in embodiments (carter / h1 / spot / g1 / digit) → just use `--embodiment ` on `run.py`. No code change needed. +- The user is adding a new **scene** (USD), not a new robot → that's `/compass` (Register Scene). +- The user has registered a robot but training fails → run `/compass-debug` first to surface the actual error. + +--- + +## Workflow + +### Step 1: Parse what the user already supplied + +A user prompt like "add a new robot called Galileo at `/workspace/galileo.usd`, similar to Spot" already contains three of the four fields the skill needs. Extract them first; only ask about what's missing or genuinely ambiguous. Don't re-ask for things the user already said — that's the dialogue-bot trap. + +The four fields: +- **Robot name** (lowercase identifier like `galileo`; used in CLI flags and config keys). +- **USD path** (absolute or repo-relative; the asset that will be spawned). +- **Base-class to inherit from** (which existing pattern to mirror). See "Base-class choice" below. +- **(Optional) display name** for docs (e.g., "Galileo"). + +### Step 2: Ask only for what's missing (`AskUserQuestion`) + +If any of the three required fields are missing or ambiguous, ask in one batch. Sample question structure: + +| Field | Question | Options | +|---|---|---| +| Robot name | "What's the robot's identifier?" | free text (default: lowercase the user's name) | +| USD path | "Where's the robot's USD file?" | free text (suggest checking `./assets/usd/` first) | +| Base-class | "Which existing robot's pattern is the best fit?" | wheeled (carter), quadruped (spot), humanoid (h1, g1, digit) | + +For the base-class question, surface the trade-off in the option descriptions: +- **wheeled**: single base, velocity over base; simplest to onboard. Mirror `carter`. +- **quadruped**: articulated legs, ABS-style controller. Mirror `spot`. +- **humanoid**: full body; more complex action space. Mirror `h1` (or `g1`/`digit` for smaller form factors). + +### Step 3: Read the existing-pattern files + +Before generating anything, read the two files that match the user's base-class choice: + +| Base class | Robot block | Env cfg file | +|---|---|---| +| wheeled | `carter` block in `compass/rl_env/exts/mobility_es/mobility_es/config/robots.py` | `compass/rl_env/exts/mobility_es/mobility_es/config/carter_env_cfg.py` | +| quadruped | `spot` block in same `robots.py` | `compass/rl_env/exts/mobility_es/mobility_es/config/spot_env_cfg.py` | +| humanoid | `h1` block in same `robots.py` | `compass/rl_env/exts/mobility_es/mobility_es/config/h1_env_cfg.py` | + +Read both top-to-bottom. Don't skim — joint names, action term wiring, and reward shaping all come from this file. Confirming the reference exists before writing keeps you from generating content based on outdated assumptions. + +### Step 4: Show the diff before writing + +Generate a preview of what will be created or modified: + +1. **New ArticulationCfg block** to be appended to `compass/rl_env/exts/mobility_es/mobility_es/config/robots.py`. Mirror the matching reference, swap robot name + USD path + joint config. +2. **New file**: `compass/rl_env/exts/mobility_es/mobility_es/config/_env_cfg.py`. Mirror the matching reference; swap class name (e.g., `GoalReachingEnvCfg`), `scene.robot` to point at the new ArticulationCfg, action term to whatever joint controller fits. +3. **Edit `run.py`**: + - Add import: `from mobility_es.config._env_cfg import GoalReachingEnvCfg`. + - Add row to `EmbodimentEnvCfgMap`: `'': GoalReachingEnvCfg,` (around line 116–122). +4. **Optional gin update**: if the user wants `` to be the default embodiment, edit `configs/shared.gin`'s `embodiment` field. By default, leave existing default in place and let the user pass `--embodiment ` on the CLI. + +Show the user each preview block (or unified diff). Ask for confirmation before writing. + +Anti-pattern guard: never silently write multi-file scaffolding. Robot onboarding is high-blast-radius — wrong joint counts or an inverted action sign can cause the robot to flail into the floor in simulation. The diff-then-confirm gate is non-negotiable. + +### Step 5: Apply the changes + +After confirmation, write the files and edit `run.py`. Use the `Edit` tool for `run.py` (one Edit per insertion: import + map row). Use `Write` for the new env_cfg.py. For `robots.py`, use `Edit` to append the new ArticulationCfg block at the end (read the file first to find the right insertion point — the last existing config). + +### Step 6: Smoke test + +Verify the robot spawns by running training with `--num_envs 1` and **without** `--headless` so the user can see the robot appear: + +```bash +python run.py \ + -c configs/train_config.gin \ + --enable_cameras \ + -o /tmp/_smoke \ + -b ./assets/x_mobility.ckpt \ + --logger tensorboard \ + --embodiment \ + --environment warehouse_single_rack \ + --num_envs 1 +``` + +Use `run_in_background: true`. Tell the user to watch the GUI: +1. Robot spawns at the configured initial pose (typically (0, 0, ~1.0)). +2. Robot doesn't immediately fall through the floor (z-init too low) or shoot up (z-init too high). +3. First PPO iteration completes without joint-controller errors in the log. + +If the robot misbehaves, route to `/compass-debug` for a deeper diagnostic. Common first-onboarding issues: +- Z-init too low → robot clips through floor → bump `init_state.pos[2]`. +- Joint names mismatch → ArticulationCfg fails to load → recheck against the USD's joint hierarchy. +- Action term sign flipped → robot drives backwards → invert sign in the action term. + +### Step 7: Document the new embodiment (optional) + +Once smoke test passes, suggest the user add their robot to `docs/handbook/extending.md` (under "Built-in embodiments" or as a new subsection). This is optional — many users keep their custom embodiment private. Don't push the doc change if the user is in a hurry; just flag it for later. + +--- + +## Reference: file map + +| File | What changes | +|------|---| +| `compass/rl_env/exts/mobility_es/mobility_es/config/robots.py` | Append new `ArticulationCfg` block | +| `compass/rl_env/exts/mobility_es/mobility_es/config/_env_cfg.py` | New file (mirror matching base-class reference) | +| `run.py` | Add import; add `EmbodimentEnvCfgMap` row (~L116–122) | +| `configs/shared.gin` | Optional default override; usually leave alone | +| `docs/handbook/extending.md` | Optional doc update (suggest, don't enforce) | + +## Reference: base-class examples + +- **Wheeled (carter)**: see `carter` block at `robots.py:23` and `carter_env_cfg.py`. +- **Quadruped (spot)**: see `spot` block at `robots.py:275` and `spot_env_cfg.py`. +- **Humanoid (h1)**: see `h1` block at `robots.py:72` and `h1_env_cfg.py`. + +For canonical written guidance (joint config, controller selection, reward shaping), the handbook's [`extending.md`](../../../docs/handbook/extending.md) covers depth this skill won't replicate. diff --git a/.claude/skills/compass/SKILL.md b/.claude/skills/compass/SKILL.md new file mode 100644 index 0000000..02eb502 --- /dev/null +++ b/.claude/skills/compass/SKILL.md @@ -0,0 +1,400 @@ +--- +name: compass +description: > + Front-door for COMPASS — training, evaluation, SAGE scene workflows + (search / USD conversion / scene registration), and OSMO cloud + submission. Use whenever the user mentions training a policy, adding + a SAGE scene, evaluating a checkpoint, or running COMPASS in general. + For deploy / debug / onboarding-a-new-robot, see the specialty + siblings: compass-deploy, compass-debug, compass-newembodiment. +allowed-tools: + - Bash + - Read + - Edit + - Write + - Grep + - Glob + - AskUserQuestion + - WebFetch + - Agent +--- + +You orchestrate the COMPASS robot navigation training pipeline — from scene search to trained policy. Adapt to context: interactive and explanatory during setup, autonomous and efficient for repeated operations like training runs. + +COMPASS trains cross-embodiment navigation policies using residual RL on top of X-Mobility (a pretrained VLA). SAGE-10k provides 10,000 pre-generated indoor scenes across 50 room types. + +## Skill Scripts + +This skill bundles two helper scripts in its own directory: +- `scripts/sage10k_search.py` — search SAGE-10k dataset by text query +- `scripts/sage10k_to_usd.py` — convert SAGE-10k scenes to USD (uses only Isaac Sim native APIs, no extra deps) + +Use the full path when invoking them: +``` +/scripts/sage10k_search.py +/scripts/sage10k_to_usd.py +``` + +## Workflow Routing + +| Workflow | Trigger | +|----------|---------| +| **Search SAGE-10k** | User wants a scene from the SAGE-10k dataset (default for new scenes) | +| **Setup COMPASS** | User wants to install COMPASS deps, or deps are missing when needed | +| **Setup SAGE (local)** | User wants full local SAGE installation for custom generation (rare; see `references/setup-sage-local.md`) | +| **Register Scene** | User has a USD file to add to COMPASS (auto-triggered after conversion) | +| **Train** | User wants to run residual RL training on a scene | +| **Evaluate** | User wants to evaluate a trained checkpoint | +| **Full Pipeline** | User gives a scene description — chain: search SAGE-10k → download → convert USD → verify in viewer → register → preview train (num_envs=1, GUI) → full train | + +If the user gives a scene description like "a cluttered warehouse" or "bedroom", treat it as **Full Pipeline** using SAGE-10k search. Use local SAGE only when the user explicitly asks for it. + +Before any of Train, Evaluate, or Full Pipeline, run the **Prerequisites Check** first. The cost of a stale check is a confusing CUDA error 30 minutes into a training run. + +## Specialty skills + +Some user intents are better handled by sibling skills. When the user's task fits one of these, **say so to the user and recommend the matching specialty** — don't try to handle it inside `compass`: + +| User intent | Specialty skill | +|---|---| +| Deploy a TRAINED checkpoint to a robot (ONNX → TRT → ROS2) | `/compass-deploy` | +| Diagnose why training won't start, "what's wrong", quick health check | `/compass-debug` | +| Add a new robot platform (cfg files, EmbodimentEnvCfgMap registration) | `/compass-newembodiment` | + +Tell the user the matching specialty and suggest they invoke it (or rephrase their ask so the auto-router picks it up). Don't programmatically invoke the sibling via the Skill tool — that adds latency and removes the user's ability to redirect. + +--- + +## Prerequisites Check + +Run this before any operation. The skill assumes the user has run `source ./docker/activate` (the docker-as-venv shim from the `docker/` subdir). Run all checks in parallel where possible. Report what's ready and what's missing. + +```bash +# Container running? +./docker/run.sh status + +# Activated shell? `deactivate` is a shell function defined by ./docker/activate +command -v deactivate >/dev/null && echo "shell: activated" || echo "shell: NOT activated — run: source ./docker/activate" + +# GPU (use dangerouslyDisableSandbox: true) +nvidia-smi --query-gpu=name,memory.total,memory.used --format=csv,noheader + +# Assets present +test -f ./assets/x_mobility.ckpt && echo "x_mobility ckpt: OK ($(du -h ./assets/x_mobility.ckpt | cut -f1))" +ls ./assets/usd/ 2>/dev/null | head -5 +``` + +If anything fails, route to **Setup COMPASS**. If the user reports a vague "training won't start" issue, that's `compass-debug`'s job — recommend it. + +--- + +## Execution Environment Rules + +These rules apply to every command. Each one exists for a specific reason; the explanation matters more than the rule itself, because edge cases need judgment. + +1. **Activated-shell rule.** Every Python invocation runs inside a shell where the user has already done `source ./docker/activate`. Inside that shell, `python`, `pip`, `tensorboard`, etc. are shims that route into the COMPASS container automatically. The right command is just `python run.py …` — no Isaac Lab launcher prefix, no conda env wrapper. If `command -v deactivate` returns nothing, the shell isn't activated — pause and ask the user to source the activate script before continuing. + +2. **GPU access needs `dangerouslyDisableSandbox: true`.** The Claude Code sandbox blocks NVIDIA driver access — without this flag, `nvidia-smi` fails and Isaac Sim can't find CUDA. This is a Claude Code concern, not a container concern, so it applies even though the GPU work happens inside the container. + +3. **Background execution for long jobs.** Training and evaluation can run for hours. Use `run_in_background: true` so the user isn't blocked. To check progress, look at log files instead of stdout (the shim forwards stdout but it's easy to miss when the run is backgrounded): + ```bash + # Find latest training log + ls -t /tmp/isaaclab/logs/ | head -1 + # Or check the Kit log for errors + find ~/.local/share/ov/pkg/isaac-sim-* -name "kit_*.log" 2>/dev/null | head -1 | xargs tail -100 + ``` + +4. **Killing processes.** `kill -9 ` of the python process. The shim forwards SIGTERM cleanly into the container, no orphan-conda-wrapper issues to worry about: + ```bash + ps aux | grep "run.py.*" | grep -v grep | awk '{print $2}' | xargs kill -9 + ``` + +--- + +## Setup COMPASS + +The new dev environment uses Docker as a venv: build the image once, download assets once, then activate. Three commands: + +```bash +export HF_TOKEN=hf_xxx # https://huggingface.co/settings/tokens +./docker/run.sh build # ~10 min cold build of compass-rl image +./docker/run.sh assets # ~5 min: downloads compass_usds + x_mobility ckpt +source ./docker/activate # prompt becomes (compass-rl) +``` + +After this, `python run.py …` (and `pip`, `tensorboard`, etc.) Just Work — they route to the container via `docker exec`. No conda env, no manual pip-install of requirements, no Isaac Lab clone. Assets land at `./assets/usd/` (built-in scenes) and `./assets/x_mobility.ckpt` (base policy), bind-mounted into the container at the same paths under `/workspace/COMPASS/`. + +Full reference: `docs/handbook/installation/docker.md`. If the user wants the bare-metal install (no Docker), point them at `docs/handbook/installation/bare-metal.md` — supported but slower to set up and not the recommended default. + +--- + +## Search SAGE-10k (recommended for new scenes) + +The SAGE-10k dataset (`nvidia/SAGE-10k`) has 10,000 pre-generated indoor scenes across 50 room types. No SAGE installation needed. + +### Step 1: Search for matching scenes + +```bash +python /scripts/sage10k_search.py "" --top 5 --sample-size 50 +``` + +First run builds a scene index from the HF API (cached afterward). Results show room type, style, object count, and description. + +### Step 2: Present options to user + +Show top results. Let user pick one. + +### Step 3: Download the selected scene + +```bash +huggingface-cli download nvidia/SAGE-10k --repo-type dataset \ + --include "scenes/" --local-dir ./sage_10k_cache + +mkdir -p ./sage_10k_scenes/ +unzip ./sage_10k_cache/scenes/ -d ./sage_10k_scenes// +``` + +### Step 4: Convert to USD + +The bundled converter only needs Isaac Sim native APIs, no extra deps: + +```bash +# dangerouslyDisableSandbox: true (Isaac Sim needs GPU) +python /scripts/sage10k_to_usd.py \ + ./sage_10k_scenes// \ + ./compass/rl_env/exts/mobility_es/mobility_es/usd//.usd +``` + +The output USD lives under the mobility_es extension directory because that's where `environments.py` expects to find it (the registered `USD_PATHS` dict resolves `../usd//...` relative to the config/ subdir). Built-in COMPASS scenes from `./assets/usd/` are referenced separately; user-added SAGE scenes go here. + +The script: +- Initializes Isaac Sim headless for `pxr` access +- Parses PLY meshes with proper binary format (separate vertex/texcoord elements) +- Scales objects to match layout dimensions +- Creates walls and floors from room geometry +- Applies textures with UV mapping +- Sets all objects as static collision (appropriate for navigation training) + +### Step 5: Verify USD in Isaac Sim viewer + +Launch the Isaac Sim viewer after conversion so the user can visually verify the scene looks correct (geometry, collisions, textures) before proceeding. Use `dangerouslyDisableSandbox: true` and `run_in_background: true`: + +```bash +python -c " +from isaacsim import SimulationApp +app = SimulationApp({'headless': False, 'width': 1280, 'height': 720}) +import omni +omni.usd.get_context().open_stage('./compass/rl_env/exts/mobility_es/mobility_es/usd//.usd') +while app.is_running(): + app.update() +app.close() +" & +``` + +Tell the user the viewer is open and to close it when done. Suggest enabling **Show > Physics > Colliders** to inspect collision meshes. Ask if the scene looks correct before proceeding. + +### Step 6: Register and train + +Proceed to **Register Scene** → **Train**. + +--- + +## Register Scene + +Edit two files. Read each first to find the right insertion point — line numbers drift over time. + +### File 1: `compass/rl_env/exts/mobility_es/mobility_es/config/environments.py` + +Add to `USD_PATHS` dict: +```python +'': + os.path.join(os.path.dirname(__file__), "../usd//.usd"), +``` + +Add scene config at end of file: +```python + = EnvSceneAssetCfg( + prim_path="{ENV_REGEX_NS}/", + init_state=AssetBaseCfg.InitialStateCfg( + pos=(0, 0, 0.01), + rot=(1.0, 0.0, 0.0, 0.0), + ), + spawn=sim_utils.UsdFileCfg( + usd_path=USD_PATHS[''], + scale=(1.0, 1.0, 1.0), + rigid_props=sim_utils.RigidBodyPropertiesCfg( + disable_gravity=None, + solver_position_iteration_count=4, + solver_velocity_iteration_count=1, + ), + ), + pose_sample_range={"x": (-3, 3), "y": (-3, 3), "yaw": (-3.14, 3.14)}, + env_spacing=20, +) +``` + +For SAGE-10k rooms (typically 4–6m), `pose_sample_range` ±3m and `env_spacing` 20 work well. Larger scenes need both bumped up proportionally. + +### File 2: `run.py` + +Add to `EnvSceneAssetCfgMap` (around line 118–128): +```python +'': environments., +``` + +--- + +## Train + +### Step 1: Preview training (always do this first) + +Launch with `--num_envs 1` and **without** `--headless`, so the user can verify the robot spawns correctly in the scene and the environment looks right. Use `run_in_background: true`: + +```bash +python run.py \ + -c configs/train_config.gin \ + --enable_cameras \ + -o _preview \ + -b ./assets/x_mobility.ckpt \ + --logger tensorboard \ + --video \ + --embodiment \ + --environment \ + --num_envs 1 +``` + +Tell the user the preview is running with the GUI open. Ask them to verify: +1. The robot spawns in a valid location (not clipping through objects). +2. The scene geometry and collisions look correct. +3. The robot can navigate without issues. + +Once the user confirms the preview, kill it and proceed to full-scale training. + +### Step 2: Full-scale training + +After preview confirmation, launch the full run with `run_in_background: true`: + +```bash +python run.py \ + -c configs/train_config.gin \ + --enable_cameras \ + -o \ + -b ./assets/x_mobility.ckpt \ + -n \ + -r \ + --logger tensorboard \ + --headless \ + --video \ + --embodiment \ + --environment \ + --num_envs +``` + +Drop `--headless` if the user wants GUI mode. For GUI mode with SAGE-10k scenes (many meshes), use `--num_envs 1` to avoid GPU OOM. + +### OSMO cluster submission + +The Python launcher in [osmo/run_osmo.py](../../../osmo/run_osmo.py) handles build+push+submit and is the recommended entry point: + +```bash +export WANDB_API_KEY= +export HF_TOKEN= +export COMPASS_OSMO_REGISTRY=nvcr.io// + +python osmo/run_osmo.py train \ + --experiment-name \ + --wandb-project +``` + +The X-Mobility base checkpoint is now downloaded inside the workflow from `huggingface.co/nvidia/X-Mobility`, so no `--base-policy-ckpt` flag is needed. For direct `osmo workflow submit` invocation, see the [OSMO cloud submission](https://nvlabs.github.io/COMPASS/docs/osmo.html) handbook page. Workflow YAMLs live in [osmo/workflows/](../../../osmo/workflows/). + +### Defaults +| Parameter | Default | Source | +|-----------|---------|--------| +| num_iterations | 1000 | train_config.gin | +| num_envs | 64 | train_config.gin | +| num_steps_per_iteration | 256 | shared.gin | +| seed | 20 | train_config.gin | +| embodiment | g1 | shared.gin | + +### Valid embodiments +`carter`, `h1`, `spot`, `g1`, `digit` + +### Built-in environments +`warehouse_single_rack`, `galileo_lab`, `simple_office`, `combined_single_rack`, `combined_multi_rack`, `random_envs`, `hospital`, `warehouse_multi_rack` + +### Outputs +- Checkpoints: `/model_*.pt` +- Videos: `/videos/` +- Logs: TensorBoard in `/` or W&B + +--- + +## Evaluate + +```bash +CKPT=$(ls /model_*.pt | sort -V | tail -n 1) + +python run.py \ + -c configs/eval_config.gin \ + --enable_cameras \ + -o \ + -b ./assets/x_mobility.ckpt \ + -p $CKPT \ + -n \ + -r eval_ \ + --logger tensorboard \ + --headless \ + --video \ + --video_interval 1 \ + --embodiment \ + --environment +``` + +--- + +## Visualize Scene in Isaac Sim + +To inspect a USD scene before training (check collision meshes, layout, etc.): +```bash +python -c " +from isaacsim import SimulationApp +app = SimulationApp({'headless': False, 'width': 1280, 'height': 720}) +import omni +omni.usd.get_context().open_stage('') +while app.is_running(): + app.update() +app.close() +" & +``` +Use `dangerouslyDisableSandbox: true`. Suggest the user enable **Show > Physics > Colliders** to verify collision meshes. + +--- + +## Setup SAGE (local — advanced) + +Most users do **not** need this. SAGE-10k search (above) covers the typical case without any SAGE install. Only use a local SAGE install when the user explicitly wants to generate fully custom scenes (novel layouts not in SAGE-10k). + +For installation steps, dependencies, and trade-offs, see `references/setup-sage-local.md` — that file is loaded only when a local SAGE install is actually needed, to keep the main flow lean. + +--- + +## Key File Locations + +| File | Purpose | +|------|---------| +| `docker/run.sh` | Build / assets / up / down / exec / shell / status | +| `docker/activate` | Sourceable activate script (sets up python/pip shims) | +| `./assets/x_mobility.ckpt` | X-Mobility base policy (downloaded by `./docker/run.sh assets`) | +| `./assets/usd/` | Built-in scene USDs (downloaded by `./docker/run.sh assets`) | +| `run.py` | Main entry point + `EnvSceneAssetCfgMap` (around lines 118–128) | +| `compass/rl_env/exts/mobility_es/mobility_es/config/environments.py` | Scene definitions, `USD_PATHS`, `EnvSceneAssetCfg` class | +| `compass/rl_env/exts/mobility_es/mobility_es/usd/` | User-added scene USDs (e.g., SAGE-10k conversions) | +| `configs/train_config.gin` | Training parameters | +| `configs/eval_config.gin` | Evaluation parameters | +| `configs/shared.gin` | Shared config (embodiment, environment, steps) | +| `osmo/workflows/rl_es_train_workflow.yaml` | OSMO training workflow template | +| `osmo/run_osmo.py` | Python launcher for OSMO submission (train / eval / record / distill) | + +## W&B Artifact Format +`//:` — e.g. `//x_mobility:v1` diff --git a/.claude/skills/compass/references/setup-sage-local.md b/.claude/skills/compass/references/setup-sage-local.md new file mode 100644 index 0000000..6b75ea8 --- /dev/null +++ b/.claude/skills/compass/references/setup-sage-local.md @@ -0,0 +1,40 @@ +# Setup SAGE (local install) + +Most COMPASS users do **not** need this. The default flow uses +`nvidia/SAGE-10k` (10,000 pre-generated indoor scenes) via the bundled +`scripts/sage10k_search.py` and `scripts/sage10k_to_usd.py` — no SAGE +install required, no extra deps beyond Isaac Sim. + +You only need a local SAGE install when you want to **generate fully +custom scenes** that aren't in SAGE-10k — e.g., novel layouts, custom +object distributions, or VLM-driven asset placement. + +## Why this is gated behind a reference file + +- Heavy deps: pytorch3d, TRELLIS, VLM servers (Llava / similar). +- Multi-GPU recommended for the generation pipeline (the VLM and the + 3D-asset generator both want a GPU). +- Cold install can take 30–60 minutes and conflicts with parts of the + COMPASS env (different torch/cuda combinations). + +For day-to-day SAGE-driven training, prefer the SAGE-10k flow in the +main `SKILL.md` (the "Search SAGE-10k" section). + +## How to install + +Follow the canonical instructions in the SAGE repo: . + +The repo README is the source of truth — version-pinning, environment +setup, and dataset paths change there faster than this reference can +keep up. Cross-link from there back to COMPASS once SAGE is installed +and you have a working `sage` CLI. + +## After SAGE is installed + +The `compass` skill's SAGE-10k flow remains the recommended path for +most scenes. If you specifically want SAGE to generate a novel layout: + +1. Run SAGE's generator to produce a layout (PLY meshes + `layout.json`). +2. Convert with the same `scripts/sage10k_to_usd.py` the SAGE-10k flow + uses — the converter is layout-source-agnostic. +3. Continue with **Register Scene** → **Train** in the main `SKILL.md`. diff --git a/.claude/skills/compass/scripts/sage10k_search.py b/.claude/skills/compass/scripts/sage10k_search.py new file mode 100755 index 0000000..acd726f --- /dev/null +++ b/.claude/skills/compass/scripts/sage10k_search.py @@ -0,0 +1,297 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Search SAGE-10k dataset for scenes matching a text query. + +Uses the HuggingFace API to list scenes, downloads layout JSONs on demand, +and caches them locally for fast repeated searches. + +Usage: + python scripts/sage10k_search.py "bedroom with desk" + python scripts/sage10k_search.py "warehouse" --top 10 + python scripts/sage10k_search.py --list-room-types + python scripts/sage10k_search.py "kitchen" --download --output-dir ./sage_scenes +""" + +import argparse +import json +import os +import random +import re +import sys +import zipfile + +from huggingface_hub import HfApi, hf_hub_download + +DATASET_ID = "nvidia/SAGE-10k" +DEFAULT_CACHE_DIR = os.path.join(os.path.dirname(__file__), "..", "sage_10k_cache") + + +def get_api(): + return HfApi() + + +def list_scene_files(api, cache_dir): + """List all scene zip files in the dataset, with caching.""" + index_path = os.path.join(cache_dir, "scene_index.json") + if os.path.exists(index_path): + with open(index_path) as f: + return json.load(f) + + print("Building scene index from HuggingFace API (first time only)...", file=sys.stderr) + files = [] + for item in api.list_repo_tree(DATASET_ID, path_in_repo="scenes", repo_type="dataset"): + name = getattr(item, "rfilename", None) or getattr(item, "path", "") + if name.endswith(".zip"): + files.append(name) + + os.makedirs(cache_dir, exist_ok=True) + with open(index_path, "w") as f: + json.dump(files, f) + print(f"Indexed {len(files)} scenes.", file=sys.stderr) + return files + + +def extract_layout_id(zip_filename): + """Extract layout ID from zip filename like '20251213_020526_layout_84b703fb.zip'.""" + match = re.search(r"layout_([a-f0-9]+)", zip_filename) + return match.group(1) if match else os.path.splitext(os.path.basename(zip_filename))[0] + + +def download_layout_json(_api, zip_path, cache_dir): + """Download a scene zip and extract its layout JSON.""" + layout_id = extract_layout_id(zip_path) + json_cache = os.path.join(cache_dir, "layouts", f"{layout_id}.json") + + if os.path.exists(json_cache): + with open(json_cache) as f: + return json.load(f) + + local_zip = hf_hub_download( + repo_id=DATASET_ID, + filename=zip_path, + repo_type="dataset", + local_dir=os.path.join(cache_dir, "zips"), + ) + + os.makedirs(os.path.join(cache_dir, "layouts"), exist_ok=True) + with zipfile.ZipFile(local_zip) as zf: + json_files = [n for n in zf.namelist() if n.endswith(".json") and "layout" in n.lower()] + if not json_files: + json_files = [n for n in zf.namelist() if n.endswith(".json")] + if json_files: + data = json.loads(zf.read(json_files[0])) + with open(json_cache, "w") as f: + json.dump(data, f) + return data + return None + + +def extract_scene_metadata(layout_data): + """Extract searchable metadata from a layout JSON.""" + meta = { + "id": layout_data.get("id", "unknown"), + "description": layout_data.get("description", ""), + "building_style": layout_data.get("building_style", ""), + "room_types": [], + "object_count": 0, + "rooms": [], + } + for room in layout_data.get("rooms", []): + room_type = room.get("room_type", "unknown") + objects = room.get("objects", []) + meta["room_types"].append(room_type) + meta["object_count"] += len(objects) + meta["rooms"].append({ + "room_type": room_type, + "object_count": len(objects), + }) + return meta + + +def score_match(query, meta): + """Score how well a scene matches the query. Higher = better match.""" + query_lower = query.lower() + query_words = set(query_lower.split()) + score = 0 + + for rt in meta["room_types"]: + rt_lower = rt.lower() + if query_lower in rt_lower or rt_lower in query_lower: + score += 10 + for word in query_words: + if word in rt_lower: + score += 5 + + desc_lower = meta["description"].lower() + for word in query_words: + if word in desc_lower: + score += 3 + + style_lower = meta["building_style"].lower() + for word in query_words: + if word in style_lower: + score += 2 + + return score + + +def search_scenes(query, scene_files, api, cache_dir, top_n=5, sample_size=100): + """Search scenes by downloading and checking layout JSONs. + + To avoid downloading all 10K scenes, we first filter by filename patterns, + then sample and download layout JSONs for detailed matching. + """ + query_lower = query.lower() + + # Phase 1: Score by filename (free, no downloads) + filename_scored = [] + for f in scene_files: + fname_lower = os.path.basename(f).lower() + fscore = 0 + for word in query_lower.split(): + if word in fname_lower: + fscore += 5 + filename_scored.append((f, fscore)) + + # Phase 2: Download layout JSONs for a sample of scenes + # Prioritize filename matches, then take random sample + filename_scored.sort(key=lambda x: -x[1]) + candidates = [f for f, s in filename_scored[:sample_size]] + + results = [] + for i, zip_path in enumerate(candidates): + print(f"\rSearching... ({i+1}/{len(candidates)})", end="", file=sys.stderr) + try: + layout = download_layout_json(api, zip_path, cache_dir) + if layout is None: + continue + meta = extract_scene_metadata(layout) + meta["zip_path"] = zip_path + score = score_match(query, meta) + if score > 0: + results.append((score, meta)) + except Exception as e: # pylint: disable=broad-exception-caught + print(f"\nWarning: failed to process {zip_path}: {e}", file=sys.stderr) + continue + + print("", file=sys.stderr) + results.sort(key=lambda x: -x[0]) + return results[:top_n] + + +def list_room_types(scene_files, api, cache_dir, sample_size=50): + """Sample scenes to discover available room types.""" + room_types = set() + sample = random.sample(scene_files, min(sample_size, len(scene_files))) + for i, zip_path in enumerate(sample): + print(f"\rSampling room types... ({i+1}/{len(sample)})", end="", file=sys.stderr) + try: + layout = download_layout_json(api, zip_path, cache_dir) + if layout: + meta = extract_scene_metadata(layout) + room_types.update(meta["room_types"]) + except Exception: # pylint: disable=broad-exception-caught + continue + print("", file=sys.stderr) + return sorted(room_types) + + +def download_scene(_api, zip_path, output_dir, cache_dir): + """Download and extract a full scene.""" + local_zip = hf_hub_download( + repo_id=DATASET_ID, + filename=zip_path, + repo_type="dataset", + local_dir=os.path.join(cache_dir, "zips"), + ) + + layout_id = extract_layout_id(zip_path) + scene_dir = os.path.join(output_dir, layout_id) + os.makedirs(scene_dir, exist_ok=True) + + with zipfile.ZipFile(local_zip) as zf: + zf.extractall(scene_dir) + + print(f"Scene extracted to: {scene_dir}") + return scene_dir + + +def main(): + parser = argparse.ArgumentParser(description="Search SAGE-10k scenes") + parser.add_argument("query", nargs="?", help="Search query (e.g. 'bedroom with desk')") + parser.add_argument("--top", type=int, default=5, help="Number of results") + parser.add_argument("--sample-size", + type=int, + default=100, + help="Number of scenes to sample for search") + parser.add_argument("--cache-dir", default=DEFAULT_CACHE_DIR, help="Cache directory") + parser.add_argument("--list-room-types", action="store_true", help="List discovered room types") + parser.add_argument("--download", action="store_true", help="Download the top result") + parser.add_argument("--output-dir", + default="./sage_10k_scenes", + help="Output directory for downloads") + parser.add_argument("--json", action="store_true", help="Output as JSON") + args = parser.parse_args() + + api = get_api() + scene_files = list_scene_files(api, args.cache_dir) + + if args.list_room_types: + types = list_room_types(scene_files, api, args.cache_dir) + print("Discovered room types:") + for t in types: + print(f" - {t}") + return + + if not args.query: + parser.error("Please provide a search query or use --list-room-types") + + results = search_scenes(args.query, + scene_files, + api, + args.cache_dir, + top_n=args.top, + sample_size=args.sample_size) + + if not results: + print("No matching scenes found. Try a broader query or increase --sample-size.") + return + + if args.json: + print(json.dumps([{"score": s, **m} for s, m in results], indent=2)) + else: + print(f"\nTop {len(results)} matches for '{args.query}':\n") + for i, (_, meta) in enumerate(results, 1): + room_types = ", ".join(meta["room_types"]) or "unknown" + print(f" {i}. {meta['id']}") + print(f" Room types: {room_types}") + print(f" Style: {meta['building_style'] or 'N/A'}") + print(f" Objects: {meta['object_count']}") + print(f" Description: {meta['description'][:100]}..." if len( + meta.get('description', '')) > + 100 else f" Description: {meta['description'] or 'N/A'}") + print(f" File: {meta['zip_path']}") + print() + + if args.download and results: + _, top_meta = results[0] + print(f"Downloading top result: {top_meta['id']}...") + scene_dir = download_scene(api, top_meta["zip_path"], args.output_dir, args.cache_dir) + print(f"Done. Scene at: {scene_dir}") + + +if __name__ == "__main__": + main() diff --git a/.claude/skills/compass/scripts/sage10k_to_usd.py b/.claude/skills/compass/scripts/sage10k_to_usd.py new file mode 100755 index 0000000..e15d490 --- /dev/null +++ b/.claude/skills/compass/scripts/sage10k_to_usd.py @@ -0,0 +1,359 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert a SAGE-10k scene to a single USD file for COMPASS training. + +Uses only Isaac Sim native APIs (pxr) and numpy — no extra dependencies. +Reads the layout JSON + PLY objects and creates a simulation-ready USD scene. + +Usage: + # Must run inside Isaac Sim environment + conda run -n env_isaaclab python scripts/sage10k_to_usd.py \ + + +Example: + conda run -n env_isaaclab python scripts/sage10k_to_usd.py \ + ./sage_10k_scenes/teen_bedroom/layout_ed24dd11.json \ + ./compass/rl_env/exts/mobility_es/mobility_es/usd/teen_bedroom/teen_bedroom.usd +""" + +import argparse +import json +import math +import os +import struct + +import numpy as np + +# Initialize Isaac Sim before importing pxr +from isaacsim import SimulationApp + +_app = SimulationApp({"headless": True}) + + +def parse_ply(ply_path): + """Parse a SAGE-10k PLY file. + + These PLYs have separate vertex and texcoord elements, and faces with + both vertex_indices and texcoord_indices lists. + + Returns: (verts, faces, uvs, face_uvs) + verts: (N, 3) float32 vertex positions + faces: (F, 3) int32 vertex indices per face + uvs: (M, 2) float32 texture coordinates (separate element) + face_uvs: (F, 3) int32 texcoord indices per face, or None + """ + with open(ply_path, "rb") as f: + # Parse header + header_lines = [] + while True: + line = f.readline().decode("ascii").strip() + header_lines.append(line) + if line == "end_header": + break + + n_verts = 0 + n_texcoords = 0 + n_faces = 0 + is_binary = any("binary_little_endian" in l for l in header_lines) + + for line in header_lines: + parts = line.split() + if len(parts) >= 3 and parts[0] == "element": + if parts[1] == "vertex": + n_verts = int(parts[2]) + elif parts[1] == "texcoord": + n_texcoords = int(parts[2]) + elif parts[1] == "face": + n_faces = int(parts[2]) + + # Count face property lists to know how many index lists per face + face_list_count = sum(1 for l in header_lines + if l.startswith("property list") and header_lines.index(l) > next( + i for i, x in enumerate(header_lines) if "element face" in x)) + + if is_binary: + # Read vertices (x, y, z as float32) + verts = np.frombuffer(f.read(n_verts * 12), dtype=np.float32).reshape(n_verts, 3).copy() + + # Read texcoords (s, t as float32) + uvs = None + if n_texcoords > 0: + uvs = np.frombuffer(f.read(n_texcoords * 8), + dtype=np.float32).reshape(n_texcoords, 2).copy() + + # Read faces + faces = [] + face_uvs = [] + for i in range(n_faces): + # First list: vertex_indices + n = struct.unpack("= 2: + n2 = struct.unpack("= 4: + # Triangulate quads + faces.append((vi[0], vi[1], vi[2])) + faces.append((vi[0], vi[2], vi[3])) + if ti and len(ti) >= 4: + face_uvs.append((ti[0], ti[1], ti[2])) + face_uvs.append((ti[0], ti[2], ti[3])) + + faces = np.array(faces, dtype=np.int32) + face_uvs = np.array(face_uvs, dtype=np.int32) if face_uvs else None + else: + raise NotImplementedError("Only binary PLY supported") + + return verts, faces, uvs, face_uvs + + +def load_layout(json_path): + """Load and parse a SAGE-10k layout JSON.""" + with open(json_path) as f: + return json.load(f) + + +def create_usd_scene(layout_data, layout_dir, output_usd_path): + """Create a USD scene from SAGE layout data using pxr APIs.""" + from pxr import Gf, Usd, UsdGeom, Vt, UsdPhysics, PhysxSchema, Sdf, UsdShade # pylint: disable=import-outside-toplevel + + os.makedirs(os.path.dirname(output_usd_path), exist_ok=True) + + stage = Usd.Stage.CreateNew(output_usd_path) + UsdGeom.SetStageUpAxis(stage, UsdGeom.Tokens.z) + UsdGeom.SetStageMetersPerUnit(stage, 1.0) + + world = UsdGeom.Xform.Define(stage, "/World") + stage.SetDefaultPrim(world.GetPrim()) + + # NOTE: No GroundPlane or PhysicsScene here — COMPASS provides its own + # terrain plane and physics scene. Adding them causes duplicate collision + # surfaces that trigger illegal_contact termination on wheeled robots. + + obj_count = 0 + objects_dir = os.path.join(layout_dir, "objects") + + for room in layout_data.get("rooms", []): + room_id = room.get("id", "room") + room_path = f"/World/Room_{room_id[:8]}" + UsdGeom.Xform.Define(stage, room_path) + + # Process walls + for wall in room.get("walls", []): + wall_id = wall.get("id", "wall")[:8] + sp = wall.get("start_point", {}) + ep = wall.get("end_point", {}) + h = wall.get("height", 2.7) + t = wall.get("thickness", 0.1) + + sx, sy = sp.get("x", 0), sp.get("y", 0) + ex, ey = ep.get("x", 0), ep.get("y", 0) + + dx, dy = ex - sx, ey - sy + length = math.sqrt(dx * dx + dy * dy) + if length < 0.01: + continue + + # Create wall as a box mesh + nx, ny = -dy / length, dx / length + hw = t / 2 + pts = [ + Gf.Vec3f(sx - nx * hw, sy - ny * hw, 0), + Gf.Vec3f(ex - nx * hw, ey - ny * hw, 0), + Gf.Vec3f(ex + nx * hw, ey + ny * hw, 0), + Gf.Vec3f(sx + nx * hw, sy + ny * hw, 0), + Gf.Vec3f(sx - nx * hw, sy - ny * hw, h), + Gf.Vec3f(ex - nx * hw, ey - ny * hw, h), + Gf.Vec3f(ex + nx * hw, ey + ny * hw, h), + Gf.Vec3f(sx + nx * hw, sy + ny * hw, h), + ] + faces_idx = [0, 1, 2, 3, 4, 7, 6, 5, 0, 4, 5, 1, 2, 6, 7, 3, 0, 3, 7, 4, 1, 5, 6, 2] + face_counts = [4, 4, 4, 4, 4, 4] + + wall_mesh = UsdGeom.Mesh.Define(stage, f"{room_path}/wall_{wall_id}") + wall_mesh.CreatePointsAttr(Vt.Vec3fArray(pts)) + wall_mesh.CreateFaceVertexCountsAttr(Vt.IntArray(face_counts)) + wall_mesh.CreateFaceVertexIndicesAttr(Vt.IntArray(faces_idx)) + wall_prim = wall_mesh.GetPrim() + UsdPhysics.CollisionAPI.Apply(wall_prim) + PhysxSchema.PhysxCollisionAPI.Apply(wall_prim) + + # Process floor + dims = room.get("dimensions", {}) + rw = dims.get("width", 5) + rl = dims.get("length", 5) + rpos = room.get("position", {}) + rx, ry = rpos.get("x", 0), rpos.get("y", 0) + + floor_mesh = UsdGeom.Mesh.Define(stage, f"{room_path}/floor") + floor_mesh.CreatePointsAttr( + Vt.Vec3fArray([ + Gf.Vec3f(rx - rw / 2, ry - rl / 2, 0.001), + Gf.Vec3f(rx + rw / 2, ry - rl / 2, 0.001), + Gf.Vec3f(rx + rw / 2, ry + rl / 2, 0.001), + Gf.Vec3f(rx - rw / 2, ry + rl / 2, 0.001), + ])) + floor_mesh.CreateFaceVertexCountsAttr(Vt.IntArray([4])) + floor_mesh.CreateFaceVertexIndicesAttr(Vt.IntArray([0, 1, 2, 3])) + # NOTE: No collision on floor — robot drives on COMPASS terrain plane. + # Adding floor collision causes duplicate ground surfaces and resets. + + # Process objects from PLY files + for obj in room.get("objects", []): + obj_id = obj.get("source_id", obj.get("id", ""))[:8] + if not obj_id: + continue + + ply_path = os.path.join(objects_dir, f"{obj_id}.ply") + if not os.path.exists(ply_path): + continue + + try: + verts, faces, uvs, face_uvs = parse_ply(ply_path) + except Exception as e: # pylint: disable=broad-exception-caught + print(f"Warning: failed to parse {ply_path}: {e}") + continue + + # Scale PLY vertices to match the object's target dimensions + obj_dims = obj.get("dimensions", {}) + tw = obj_dims.get("width", 1.0) + tl = obj_dims.get("length", 1.0) + th = obj_dims.get("height", 1.0) + + # Compute current bounding box of PLY mesh + bbox_min = verts.min(axis=0) + bbox_max = verts.max(axis=0) + bbox_size = bbox_max - bbox_min + bbox_size = np.maximum(bbox_size, 1e-6) # avoid div by zero + + # Center the mesh at origin, then scale to target dimensions + center = (bbox_min + bbox_max) / 2 + verts = verts - center + scale = np.array([tw / bbox_size[0], tl / bbox_size[1], th / bbox_size[2]], + dtype=np.float32) + verts = verts * scale + + # Shift so bottom of mesh is at z=0 + verts[:, 2] -= verts[:, 2].min() + + pos = obj.get("position", {}) + rot = obj.get("rotation", {}) + ox = pos.get("x", 0) + oy = pos.get("y", 0) + oz = pos.get("z", 0) + rx_deg = rot.get("x", 0) + ry_deg = rot.get("y", 0) + rz_deg = rot.get("z", 0) + + obj_path = f"{room_path}/obj_{obj_id}_{obj_count}" + obj_count += 1 + + obj_mesh = UsdGeom.Mesh.Define(stage, obj_path) + obj_mesh.CreatePointsAttr(Vt.Vec3fArray.FromNumpy(verts)) + face_counts = np.full(len(faces), 3, dtype=np.int32) + obj_mesh.CreateFaceVertexCountsAttr(Vt.IntArray.FromNumpy(face_counts)) + obj_mesh.CreateFaceVertexIndicesAttr(Vt.IntArray.FromNumpy(faces.flatten())) + + # Apply transform + xformable = UsdGeom.Xformable(obj_mesh.GetPrim()) + xformable.AddTranslateOp().Set(Gf.Vec3d(ox, oy, oz)) + if rx_deg != 0: + xformable.AddRotateXOp().Set(float(rx_deg)) + if ry_deg != 0: + xformable.AddRotateYOp().Set(float(ry_deg)) + if rz_deg != 0: + xformable.AddRotateZOp().Set(float(rz_deg)) + + # Apply texture if available + tex_path = os.path.join(objects_dir, f"{obj_id}_texture.png") + if os.path.exists(tex_path) and uvs is not None: + try: + # Use face_uvs indices if available, otherwise fall back to vertex indices + if face_uvs is not None and len(face_uvs) == len(faces): + uv_indices = np.clip(face_uvs.flatten(), 0, len(uvs) - 1) + else: + uv_indices = np.clip(faces.flatten(), 0, len(uvs) - 1) + tex_coords_arr = uvs[uv_indices].reshape(-1, 2) + + mat_path = f"{obj_path}_mat" + material = UsdShade.Material.Define(stage, mat_path) + shader = UsdShade.Shader.Define(stage, f"{mat_path}/PBRShader") + shader.CreateIdAttr("UsdPreviewSurface") + shader.CreateInput("roughness", Sdf.ValueTypeNames.Float).Set(0.8) + material.CreateSurfaceOutput().ConnectToSource(shader.ConnectableAPI(), + "surface") + + st_reader = UsdShade.Shader.Define(stage, f"{mat_path}/stReader") + st_reader.CreateIdAttr("UsdPrimvarReader_float2") + st_input = material.CreateInput("frame:stPrimvarName", Sdf.ValueTypeNames.Token) + st_input.Set("st") + st_reader.CreateInput("varname", + Sdf.ValueTypeNames.Token).ConnectToSource(st_input) + + tex_sampler = UsdShade.Shader.Define(stage, f"{mat_path}/diffuseTexture") + tex_sampler.CreateIdAttr("UsdUVTexture") + tex_sampler.CreateInput("file", Sdf.ValueTypeNames.Asset).Set(tex_path) + tex_sampler.CreateInput("st", Sdf.ValueTypeNames.Float2).ConnectToSource( + st_reader.ConnectableAPI(), "result") + tex_sampler.CreateOutput("rgb", Sdf.ValueTypeNames.Float3) + shader.CreateInput("diffuseColor", Sdf.ValueTypeNames.Color3f).ConnectToSource( + tex_sampler.ConnectableAPI(), "rgb") + + primvar = UsdGeom.PrimvarsAPI(obj_mesh).CreatePrimvar( + "st", Sdf.ValueTypeNames.TexCoord2fArray, UsdGeom.Tokens.faceVarying) + primvar.Set(Vt.Vec2fArray.FromNumpy(tex_coords_arr)) + + obj_mesh.GetPrim().ApplyAPI(UsdShade.MaterialBindingAPI) + UsdShade.MaterialBindingAPI(obj_mesh).Bind(material) + except Exception as e: # pylint: disable=broad-exception-caught + print(f"Warning: texture failed for {obj_id}: {e}") + + # Add collision — all objects are static for navigation training + obj_prim = obj_mesh.GetPrim() + UsdPhysics.CollisionAPI.Apply(obj_prim) + ps_collision = PhysxSchema.PhysxCollisionAPI.Apply(obj_prim) + ps_collision.CreateContactOffsetAttr(0.005) + ps_collision.CreateRestOffsetAttr(0.001) + + stage.GetRootLayer().Save() + print(f"USD scene saved to: {output_usd_path}") + print(f" Rooms: {len(layout_data.get('rooms', []))}") + print(f" Objects loaded: {obj_count}") + return output_usd_path + + +def main(): + parser = argparse.ArgumentParser(description="Convert SAGE-10k scene to USD") + parser.add_argument("layout_json", help="Path to layout JSON file") + parser.add_argument("output_usd", help="Output USD file path") + args = parser.parse_args() + + layout_data = load_layout(args.layout_json) + layout_dir = os.path.dirname(os.path.abspath(args.layout_json)) + create_usd_scene(layout_data, layout_dir, args.output_usd) + + +if __name__ == "__main__": + main() + _app.close() diff --git a/.dockerignore b/.dockerignore index 6505ce0..e29ee71 100644 --- a/.dockerignore +++ b/.dockerignore @@ -1,2 +1,7 @@ ./compass/rl_env/exts/mobility_es/mobility_es/usd ./venv +./assets +./.cache +./.git +./.nv +./.nvidia-omniverse diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml new file mode 100644 index 0000000..fcf69e1 --- /dev/null +++ b/.github/workflows/docs.yml @@ -0,0 +1,90 @@ +# Build the COMPASS docs site (academic landing + Sphinx handbook) and deploy +# it to GitHub Pages. +# +# Triggers: +# - pull_request touching docs/ or this workflow -> build only (validates +# `make html` -W and uploads the rendered _site/ as an artifact reviewers +# can download from the run page). +# - push to main touching docs/ or this workflow -> build + deploy. +# - workflow_dispatch -> build + deploy on demand (any branch). +# +# Repo settings -> Pages -> Source must be "GitHub Actions" (not "Deploy from +# a branch") for this to take over from the legacy gh_page branch. + +name: Build and deploy docs + +on: + pull_request: + paths: + - "docs/**" + - ".github/workflows/docs.yml" + push: + branches: [main] + paths: + - "docs/**" + - ".github/workflows/docs.yml" + workflow_dispatch: {} + +permissions: + contents: read + pages: write + id-token: write + +jobs: + build: + runs-on: ubuntu-latest + steps: + - name: Check out repo + uses: actions/checkout@v4 + with: + # The Bulma project page lives under docs/project_page/ with a + # handful of LFS-tracked images and videos. Pull them so they + # actually appear in the deployed site. + lfs: true + + - name: Set up Python + uses: actions/setup-python@v5 + with: + python-version: "3.11" + + - name: Install Sphinx + nvidia-sphinx-theme + extensions + working-directory: docs/handbook + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + + - name: Build handbook (warnings -> errors) + working-directory: docs/handbook + run: make html + + - name: Stage academic landing + handbook under _site/ + run: | + mkdir -p _site + cp -r docs/project_page/. _site/ + cp -r docs/handbook/_build/html _site/docs + + - name: Configure Pages + uses: actions/configure-pages@v5 + + - name: Upload site artifact + uses: actions/upload-pages-artifact@v3 + with: + path: _site + + deploy: + # Only deploy on push to main or manual dispatch; PR builds stop after + # the artifact upload above so the PR check validates the build without + # touching the live Pages site. + if: github.event_name != 'pull_request' + needs: build + runs-on: ubuntu-latest + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + concurrency: + group: pages + cancel-in-progress: true + steps: + - name: Deploy to Pages + id: deployment + uses: actions/deploy-pages@v4 diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml new file mode 100644 index 0000000..661910e --- /dev/null +++ b/.github/workflows/pre-commit.yml @@ -0,0 +1,31 @@ +# Run the repo's pre-commit hooks on every PR + push to main. +# Hooks are pinned in .pre-commit-config.yaml (yapf, pylint, nbstripout, +# clang-format, requirements-txt-fixer, trailing-whitespace, end-of-file-fixer, +# check-added-large-files). pylint runs without project deps because +# `.pylintrc` disables `import-error`. + +name: pre-commit + +on: + pull_request: + push: + branches: [main] + +jobs: + pre-commit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + + - uses: actions/setup-python@v5 + with: + python-version: "3.11" + cache: pip + + - name: Install clang-format binary + # mirrors-clang-format @ v13.0.0 expects clang-format on PATH; install + # via apt for reproducibility (slightly faster on cold cache than the + # hook's own download). + run: sudo apt-get update && sudo apt-get install -y clang-format + + - uses: pre-commit/action@v3.0.1 diff --git a/.gitignore b/.gitignore index 68efd40..33b1d19 100644 --- a/.gitignore +++ b/.gitignore @@ -7,6 +7,7 @@ cscope.* imgui.ini .dazel_run venv/ +.venv/ .clangd/ # Local setups @@ -36,3 +37,17 @@ ros2_deployment/assets/ *.onnx *.engine *.trt + +# Docker dev environment (assets downloaded by docker/prepare_assets.sh, caches +# from container-side pip / pre-commit / huggingface routed under HOME=/workspace/COMPASS). +/assets/ +/.cache/ +/.nv/ +/.nvidia-omniverse/ + +# Sphinx handbook local build output (CI publishes from a separate _site/). +/docs/handbook/_build/ + +# Local staging dir produced by mimicking the docs.yml workflow (cp -r +# docs/project_page/. _site/ && cp -r docs/handbook/_build/html _site/docs). +/_site/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ac916c7..a617573 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -19,7 +19,7 @@ repos: - id: requirements-txt-fixer - id: trailing-whitespace - repo: https://github.com/google/yapf - rev: v0.31.0 + rev: v0.43.0 hooks: - id: yapf name: "yapf" diff --git a/CHANGELOG.md b/CHANGELOG.md index 603c53b..e20268a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,28 @@ All notable changes to this project will be documented in this file. +## [1.6.0] - TBD + +### Added +- OSMO cloud submission: `osmo/run_osmo.py` + four workflow YAMLs (train / eval / record / distillation). Assets (X-Mobility base checkpoint, COMPASS USDs) download from HuggingFace inside the workflow; W&B / HF tokens routed via env vars or interactive `--prompt`. Includes `--embodiment` / `--environment` overrides for multi-embodiment training sweeps without YAML hand-edits. +- Multi-GPU residual-RL training with `--distributed`: torchrun-launched, per-rank Isaac Sim instance, manual gradient / KL / metric all-reduce in `PPO.update`, rank-0-only logger / checkpoint / video / episode-log writes. Pairs with `osmo/run_osmo.py --num-gpus {2,8}` and `osmo/workflows/rl_es_train_8gpu_workflow.yaml`. +- Auto-generated occupancy maps from USD: `scripts/generate_omap_from_usd.py` wraps Isaac Sim's `isaacsim.asset.gen.omap`. `OccupancyMapCollisionChecker` falls back to discovering `/omap/occupancy_map.yaml` when `OMAP_PATHS` has no entry, so SAGE-driven training and new scenes ramp up without hand-tuned omaps. +- Docker-as-venv dev environment: `docker/run.sh` (build / assets / up / down / exec / shell / status subcommands) plus a sourceable `docker/activate` that transparently routes `python` / `pip` / `tensorboard` through the container. Cuts first-time setup from ~30-60 minutes to ~3 minutes. +- COMPASS docs site: Sphinx handbook (`docs/handbook/`) deployed at `nvlabs.github.io/COMPASS/docs/`; academic project page consolidated under `docs/project_page/`. Auto-deployed via `.github/workflows/docs.yml`. +- Agentic skills for Claude Code under `.claude/skills/`: `/compass` (front-door for training / eval / SAGE / OSMO) plus specialty siblings `/compass-deploy` (checkpoint → ONNX → TRT → ROS2), `/compass-debug` (8-check diagnostic with `scripts/compass_status.sh`), and `/compass-newembodiment` (interactive robot-onboarding flow). +- CI: `.github/workflows/pre-commit.yml` runs yapf / pylint / nbstripout / clang-format / large-files / trailing-whitespace / EOF / requirements-txt-fixer on every PR. +- No-regression benchmark gate: `osmo/run_benchmark.py` fires one OSMO eval workflow per scene for a given embodiment (default sweep across 5 scenes); results land in W&B for offline regression assessment. + +### Changed +- **Isaac Lab 2.1 → 3.0.0-beta1.** Breaking for downstream consumers of `mobility_es` APIs: `AdditiveUniformNoiseCfg` → `UniformNoiseCfg`; restructured physics via `isaaclab_physx.physics.PhysxCfg`; `rerender_on_reset` → `num_rerenders_on_reset`; `ActionTerm` import moves from `isaaclab.envs.mdp.actions` to `isaaclab.managers`; asset data fields (`root_pos_w`, `root_quat_w`, etc.) require `wp.to_torch()` wrapping; `write_root_state_to_sim()` split into `write_root_pose_to_sim_index()` + `write_root_velocity_to_sim_index()`; quaternion convention flipped wxyz → xyzw across `environments.py` and `NonHolonomicPerfectControlAction` yaw extraction; `velocity_limit` → `velocity_limit_sim` on carter caster joints; `commands.UniformPose2dCommand` used directly (module reorg). +- `--wandb-project-name` is now **required** in `run.py`, `record.py`, and `distillation_train.py` (previously defaulted to internal project names). +- `requirements.txt`: 17 previously-unpinned dependencies pinned to versions verified inside `compass-rl:latest`. +- `README.md` leads with the Docker quick-start; bare-metal install moved under "Manual install". +- Pre-commit `yapf` bumped to v0.43.0 for Python 3.12 compatibility (the previous v0.31.0 pin imports `lib2to3`, removed from the stdlib in 3.13). + +### Fixed +- Occupancy-map generation: wait for `omni.usd.get_context().get_stage_loading_status()` to settle (`to_load == 0`) before invoking `Generator.generate2d()` — previously crashed Kit on USDs with external references (e.g. `combined_simple_warehouse` referencing `galileo_lab.usd`). + ## [1.5.0] - 2025-07-29 ### Added diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 0000000..52204eb --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,89 @@ +# CLAUDE.md + +This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. + +## Project + +COMPASS is a cross-embodiment mobility policy framework: imitation learning + residual RL specialists per embodiment, distilled into one generalist policy, exported to ONNX/TensorRT, deployed via ROS2. Built on Isaac Lab 2.1 / Isaac Sim 4.5 and on top of NVIDIA's [X-Mobility](https://github.com/NVlabs/X-MOBILITY) base policy. + +End-to-end pipeline (each stage has a top-level script): +1. `run.py` — train residual RL specialist (or evaluate any policy) in Isaac Lab +2. `record.py` — roll out specialists to collect HDF5 distillation data +3. `distillation_train.py` — distill specialists into one generalist (PyTorch Lightning) +4. `onnx_conversion.py` — export specialist or generalist policy to ONNX + JIT +5. `trt_conversion.py` — convert ONNX → TensorRT engine +6. `ros2_deployment/` — ROS2 nodes that consume the TRT engine in Isaac Sim or on real robots + +## Critical: how to invoke Python + +Scripts that touch Isaac Lab / Isaac Sim **must** run through the Isaac Lab Python wrapper: + +```bash +${ISAACLAB_PATH}/isaaclab.sh -p run.py ... +${ISAACLAB_PATH}/isaaclab.sh -p record.py ... +${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -r requirements.txt +``` + +Pure-PyTorch scripts can use plain Python: + +```bash +python3 distillation_train.py ... +python3 onnx_conversion.py ... +python3 trt_conversion.py ... +python3 scripts/hdf5_to_lerobot_episodic.py ... +``` + +Environments are created via `python3 -m venv venv && source venv/bin/activate`, but pip installs that need Isaac Lab's site-packages still go through `isaaclab.sh -p -m pip install`. + +GPU memory scales with the number of envs in residual RL: 32 envs ≈ 30 GB. Reduce if constrained. + +## Repository layout + +- `compass/` — main Python package + - `residual_rl/` — actor-critic that adds a residual on top of the X-Mobility base policy + - `distillation/` — generalist distillation (PyTorch Lightning trainer, HDF5 data module) + - `rl_env/exts/mobility_es/` — Isaac Lab extension defining embodiments (H1, Carter, Spot, G1, Digit) and scenes; see https://nvlabs.github.io/COMPASS/docs/extending.html for adding new ones + - `utils/` — shared helpers +- `configs/` — Gin config files (`train_`, `eval_`, `record_`, `distillation_`, `shared`, `x_mobility_`) plus a YAML template (`distillation_dataset_config_template.yaml`) for selecting specialist checkpoints and split ratios when collecting distillation data +- `x_mobility/x_mobility-0.1.0-py3-none-any.whl` — vendored X-Mobility wheel (installed via pip; imported as `model.x_mobility`) +- `ros2_deployment/` — ROS2 packages and container scripts for deploying COMPASS; see https://nvlabs.github.io/COMPASS/docs/deployment/ros2.html (Isaac Sim / sim2real / object nav) and https://nvlabs.github.io/COMPASS/docs/deployment/isaac_sim.html (Isaac Sim setup) +- `docker/` — `Dockerfile.rl` and `Dockerfile.distillation` (PyTorch 24.01 base for distillation; RL training is normally run on the host with Isaac Lab installed) +- `scripts/hdf5_to_lerobot_episodic.py` — converts HDF5 distillation datasets to GR00T LeRobot format + +## External assets that must be downloaded manually + +- X-Mobility base checkpoint: https://huggingface.co/nvidia/X-Mobility/blob/main/x_mobility-nav2-semantic_action_path.ckpt +- Residual-RL environment USDs: https://huggingface.co/nvidia/COMPASS/blob/main/compass_usds.zip — unzip into `compass/rl_env/exts/mobility_es/mobility_es/usd/` + +Most scripts take the X-Mobility checkpoint via `-b/--base-policy-path` and write outputs under `-o/--output-dir`. + +## Adding a new embodiment or scene + +1. Follow https://nvlabs.github.io/COMPASS/docs/extending.html to extend the Isaac Lab `mobility_es` extension. +2. Register the new entry in `EmbodimentEnvCfgMap` and/or `EnvSceneAssetCfgMap` in `run.py`. +3. Reference it via Gin config or CLI flag. + +## Linting and formatting + +No test suite. No CI config. Quality is enforced via pre-commit (`.pre-commit-config.yaml`): + +- `yapf` — Google style, column limit 100 (`.style.yapf`) +- `pylint` v3.0.3 (`.pylintrc` — many checks disabled; max-line-length 100) +- `clang-format` for C/C++ in ROS2 packages +- `nbstripout`, `trailing-whitespace`, `end-of-file-fixer`, `requirements-txt-fixer` + +Run before committing: + +```bash +pre-commit run --all-files +``` + +## Configuration system + +Hyperparameters use [`gin-config`](https://github.com/google/gin-config), not argparse. Common pattern: pass one or more `.gin` files via `-c/--config-files`, optionally with command-line overrides. `configs/shared.gin` is included by other configs. + +Logging defaults to TensorBoard at `/tensorboard/`. Add `--logger wandb` plus `--wandb-{run,project,entity}-name` flags for W&B. + +## Commits + +All commits must be DCO-signed (`git commit -s -m "..."`). Unsigned commits are rejected. See `CONTRIBUTING.md`. diff --git a/README.md b/README.md index c0aeab1..997fdb7 100644 --- a/README.md +++ b/README.md @@ -2,13 +2,14 @@
-[![Isaac Lab](https://img.shields.io/badge/IsaacLab-2.1.0-b.svg)](https://isaac-sim.github.io/IsaacLab/v2.1.0/index.html) +[![Isaac Lab](https://img.shields.io/badge/IsaacLab-3.0.0--beta1-b.svg)](https://isaac-sim.github.io/IsaacLab/v3.0.0-beta1/index.html) [![IsaacSim](https://img.shields.io/badge/IsaacSim-4.5.0-b.svg)](https://docs.isaacsim.omniverse.nvidia.com/4.5.0/index.html) [![Linux platform](https://img.shields.io/badge/Platform-linux--64-orange.svg)](https://ubuntu.com/blog/tag/22-04-lts) [![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [[Website]](https://nvlabs.github.io/COMPASS/) +[[Documentation]](https://nvlabs.github.io/COMPASS/docs/) [[arXiv]](https://arxiv.org/abs/2502.16372)
@@ -20,258 +21,56 @@ This repository provides the official PyTorch implementation of [COMPASS](https: COMPASS

+COMPASS is a framework for cross-embodiment mobility that combines: -COMPASS is a novel framework for cross-embodiment mobility that combines: - Imitation Learning (IL) for strong baseline performance - Residual Reinforcement Learning (RL) for embodiment-specific adaptation - Policy distillation to create a unified, generalist policy -## Quick Start +## Quick start -🚀 **Get started in 3 steps:** -1. [Install Isaac Lab and dependencies](#installation) -2. [Train your own specialists or deploy on robots](#usage) -3. [Data generation for GR00T post-training](#gr00t-post-training-with-compass-datasets) - -## Installation - -
-📦 Complete Installation Guide (click to expand) - -### 1. Isaac Lab Installation -* Install Isaac Lab and the residual RL mobility extension by following this [instruction](compass/rl_env/README.md). - -### 2. Environment Setup -* Create and activate a virtual environment: - ```bash - python3 -m venv venv - source venv/bin/activate - ``` - -### 3. Dependencies -* Install the required packages: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -r requirements.txt - ``` - -### 4. X-Mobility Installation -* Install the [X-Mobility](https://github.com/NVlabs/X-MOBILITY) package: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install x_mobility/x_mobility-0.1.0-py3-none-any.whl - ``` -* Download the pre-trained X-Mobility checkpoint from: https://huggingface.co/nvidia/X-Mobility/blob/main/x_mobility-nav2-semantic_action_path.ckpt - -### 5. Residual RL environment USDs -* Download the residual RL environment USDs from: https://huggingface.co/nvidia/COMPASS/blob/main/compass_usds.zip -* Unzip and place the downloaded USDs in the `compass/rl_env/exts/mobility_es/mobility_es/usd` directory - -
- - -## Usage - -
-🤖 Training Residual RL Specialists (click to expand) - -### Residual RL Specialists - -* Train with the default configurations in `configs/train_config.gin`: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p run.py \ - -c configs/train_config.gin \ - -o \ - -b \ - --enable_camera - ``` - -* Evaluate trained model: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p run.py \ - -c configs/eval_config.gin \ - -o \ - -b \ - -p \ - --enable_camera \ - --video \ - --video_interval - ``` - -> **NOTE**: The GPU memory usage is proportional to the number of environments in residual RL training. For example, 32 environments will use around 30GB memory, so reduce the number of environments if you have limited GPU memory. - -
- -
-🧠 Policy Distillation (click to expand) - -### Policy Distillation - -* Collect specialist data: - * Update specialists policy checkpoint paths in [dataset_config_template](configs/distillation_dataset_config_template.yaml) - * Run data collection: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p record.py \ - -c configs/distillation_dataset_config_template.yaml \ - -o \ - -b \ - --dataset-name - ``` - -* Train generalist policy: - ```bash - python3 distillation_train.py \ - --config-files configs/distillation_config.gin \ - --dataset-path \ - --output-dir - ``` - -* Evaluate generalist policy: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p run.py \ - -c configs/eval_config.gin \ - -o \ - -b \ - -d \ - --enable_camera \ - --video \ - --video_interval - ``` - -
- -
-📤 Model Export (click to expand) - -### Model Export - -* Export RL specialist policy to ONNX or JIT formats: - ```bash - python3 onnx_conversion.py \ - -b \ - -r \ - -o \ - -j - ``` - -* Export generalist policy to ONNX or JIT formats: - ```bash - python3 onnx_conversion.py \ - -b \ - -g \ - -e \ - -o \ - -j - ``` - -* Convert the ONNX to TensorRT: - ```bash - python3 trt_conversion.py -o -t - ``` - -
- -
-🔧 Add New Embodiment or Scene (click to expand) - -### Add New Embodiment or Scene - -* Follow this [instruction](compass/rl_env/README.md) to add a new embodiment or scene to the Isaac Lab RL environment. -* Register the new embodiment or scene to the `EmbodimentEnvCfgMap` and `EnvSceneAssetCfgMap` in [run.py](run.py), then update the configs or use command line arguments to select the new embodiment or scene. - -
- -
-🚀 ROS2 Deployment (click to expand) - -### ROS2 Deployment - -To deploy COMPASS in Isaac Sim or on real robots using ROS2, please follow the detailed instructions in [ros2_deployment/README.md](ros2_deployment/README.md). This guide covers containerized workflows, and Isaac Sim integration. - -
- - -
-📊 Logging Options (click to expand) - -### Logging - -The training and evaluation scripts use TensorBoard for logging by default. Weights & Biases (W&B) logging is also supported for more advanced experiment tracking features. - -**To use TensorBoard (default):** -- Logs will be saved to `/tensorboard/` -- View logs with: `tensorboard --logdir=/tensorboard/` - -**To use Weights & Biases:** -1. Install and set up W&B: `pip install wandb` and follow the [setup instructions](https://docs.wandb.ai/quickstart) -2. Log in to your W&B account: `wandb login` -3. Add the `--logger wandb` flag to your command: - ```bash - ${ISAACLAB_PATH}/isaaclab.sh -p run.py \ - -c configs/train_config.gin \ - -o \ - --enable_camera \ - --logger wandb \ - --wandb-run-name "experiment_name" \ - --wandb-project-name "project_name" \ - --wandb-entity-name "your_username_or_team" - ``` - -
- - -## GR00T Post-training with COMPASS Datasets - -The COMPASS distillation datasets can also be used to train VLA models like [GR00T](https://github.com/NVIDIA/Isaac-GR00T), to enhance their navigation capabilities. - -
-🤖 GR00T Post-training Steps (click to expand) - -**Step 1: Collect the datasets and convert to GR00T Lerobot format** - -Follow the steps described above in the "🤖 Training Residual RL Specialists" and "🧠 Policy Distillation" sections to train a specialist policy and generate the corresponding specialist datasets. - -Use the following command to convert the distillation dataset from HDF5 to the GR00T Lerobot episodic format: - ```bash - python scripts/hdf5_to_lerobot_episodic.py --hdf5-dir --output-path - ``` - -**Step 2: Post-train the GR00T model** - -Once the dataset is converted, follow the post-training [instructions](https://github.com/NVIDIA/Isaac-GR00T/tree/main/getting_started) provided in the GR00T repo. A ready-to-use navigation data configuration for post-training is available in this [branch](https://github.com/NVIDIA/Isaac-GR00T/compare/main...liuw/nav_fine_tune). - -**Step 3: Evaluate the post-trained GR00T model** +```bash +git clone https://github.com/NVlabs/COMPASS.git && cd COMPASS -To evaluate the post-trained GR00T model, first launch the inference server from within the GR00T repository by following the setup instructions provided there. Ensure that the data configuration matches the one used during training, and the port number is set as 8888. +export HF_TOKEN=hf_xxx # https://huggingface.co/settings/tokens +./docker/run.sh assets # USDs + X-Mobility ckpt → ./assets/ (~5 min) +./docker/run.sh build # build the dev image (~10 min) +source ./docker/activate # venv-like activation (prompt: (compass-rl)) -Once the server is running, start the COMPASS evaluation with the following command: -```bash -${ISAACLAB_PATH}/isaaclab.sh -p run.py \ - -c configs/eval_config.gin \ - -o \ - -b \ - --enable_camera \ - --gr00t-policy +python run.py -c configs/train_config.gin -o /tmp/out -b ./assets/x_mobility.ckpt --enable_cameras ``` -You can modify the evaluation parameters in the eval_config.gin file as needed. -
+`python` is now a shim that runs inside the container. Edit code with your host +editor — the bind-mount means changes hot-reload. `deactivate` to leave; +`./docker/run.sh down` to stop the container. + +## Documentation +Everything else — install details, training / distillation / export workflows, +ROS2 deployment, OSMO cloud submission, GR00T post-training, agentic skills, +auto-OMap generation, and contributing — lives in the **handbook**: +> 📖 **** ## License + COMPASS is released under the Apache License 2.0. See [LICENSE](LICENSE) for additional details. -## Core Contributors -Wei Liu, Huihua Zhao, Chenran Li, Joydeep Biswas, Soha Pouya, Yan Chang +## Core contributors +Wei Liu, Huihua Zhao, Chenran Li, Joydeep Biswas, Soha Pouya, Yan Chang ## Acknowledgments + We would like to acknowledge the following projects where parts of the codes in this repo is derived from: + - [RSL_RL](https://github.com/leggedrobotics/rsl_rl/tree/main) - [Isaac Lab](https://github.com/isaac-sim/IsaacLab) ## Citation + If you find this work useful in your research, please consider citing: + ```bibtex @article{liu2025compass, title={COMPASS: Cross-embodiment Mobility Policy via Residual RL and Skill Synthesis}, @@ -279,3 +78,4 @@ If you find this work useful in your research, please consider citing: journal={arXiv preprint arXiv:2502.16372}, year={2025} } +``` diff --git a/compass/__init__.py b/compass/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/__init__.py +++ b/compass/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/residual_rl/__init__.py b/compass/residual_rl/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/residual_rl/__init__.py +++ b/compass/residual_rl/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/residual_rl/ppo.py b/compass/residual_rl/ppo.py index 3fc9ca7..54d9197 100644 --- a/compass/residual_rl/ppo.py +++ b/compass/residual_rl/ppo.py @@ -13,9 +13,12 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os + import gin import torch from torch import nn, optim +import torch.distributed as dist from compass.residual_rl.rollout_storage import RolloutStorage @@ -41,6 +44,7 @@ def __init__(self, max_grad_norm=1.0): self.device = device + self.world_size = int(os.environ.get("WORLD_SIZE", "1")) # PPO components self.actor_critic = actor_critic @@ -113,6 +117,8 @@ def compute_returns(self, last_states): def update(self): mean_value_loss = 0 mean_surrogate_loss = 0 + mean_entropy = 0 + last_kl_mean = 0.0 generator = self.storage.mini_batch_generator(self.num_mini_batches, self.num_learning_epochs) for states_batch, actions_batch, target_values_batch, advantages_batch, \ @@ -136,11 +142,18 @@ def update(self): (2.0 * torch.square(sigma_batch)) - 0.5, axis=-1) kl_mean = torch.mean(kl) + # Synchronize KL across ranks so every rank reaches the same LR + # adaptation decision. Without this, ranks would step the + # optimizer with different LRs on identical (averaged) gradients + # and parameters would diverge across ranks. + if self.world_size > 1: + dist.all_reduce(kl_mean, op=dist.ReduceOp.AVG) + last_kl_mean = float(kl_mean.item()) if kl_mean > self.desired_kl * 2.0: self.learning_rate = max(1e-5, self.learning_rate / 1.5) elif (self.desired_kl / 2.0) > kl_mean > 0.0: - self.learning_rate = min(1e-2, self.learning_rate * 1.5) + self.learning_rate = min(2e-3, self.learning_rate * 1.5) for param_group in self.optimizer.param_groups: param_group['lr'] = self.learning_rate @@ -166,21 +179,48 @@ def update(self): loss = surrogate_loss + self.value_loss_coef * value_loss - \ self.entropy_coef * entropy_batch.mean() - # Gradient step + # Gradient step. Canonical DDP order: backward → all-reduce → clip → + # step. The averaged gradient is what the optimizer applies, so clip + # the averaged gradient (not the per-rank one) — otherwise per-rank + # clipping followed by averaging across disagreeing ranks shrinks + # the gradient by ~1/sqrt(world_size) on top of the bound, which + # with PPO's value-loss-dominated norm crushes the actor signal. self.optimizer.zero_grad() loss.backward() + if self.world_size > 1: + for p in self.actor_critic.parameters(): + if p.grad is not None: + dist.all_reduce(p.grad, op=dist.ReduceOp.AVG) nn.utils.clip_grad_norm_(self.actor_critic.parameters(), self.max_grad_norm) self.optimizer.step() mean_value_loss += value_loss.item() mean_surrogate_loss += surrogate_loss.item() + mean_entropy += float(entropy_batch.mean().item()) num_updates = self.num_learning_epochs * self.num_mini_batches mean_value_loss /= num_updates mean_surrogate_loss /= num_updates + mean_entropy /= num_updates + # Average reported metrics across ranks so rank-0's logger reflects global means. + if self.world_size > 1: + t = torch.tensor([mean_value_loss, mean_surrogate_loss, mean_entropy], + device=self.device) + dist.all_reduce(t, op=dist.ReduceOp.AVG) + mean_value_loss = t[0].item() + mean_surrogate_loss = t[1].item() + mean_entropy = t[2].item() self.storage.clear() - return mean_value_loss, mean_surrogate_loss + # Diagnostics dict for the trainer to log. action std is a parameter + # (same on every rank by construction), so no all-reduce needed. + diagnostics = { + "ppo/learning_rate": self.learning_rate, + "ppo/kl_mean": last_kl_mean, + "ppo/entropy": mean_entropy, + "ppo/action_std_mean": float(self.actor_critic.std.detach().mean().item()), + } + return mean_value_loss, mean_surrogate_loss, diagnostics def _get_policy_states(self, states): return states[0] diff --git a/compass/residual_rl/residual_ppo_trainer.py b/compass/residual_rl/residual_ppo_trainer.py index 5b916cc..d013ac2 100644 --- a/compass/residual_rl/residual_ppo_trainer.py +++ b/compass/residual_rl/residual_ppo_trainer.py @@ -14,11 +14,14 @@ # limitations under the License. import os +import time +from contextlib import contextmanager import numpy as np import h5py import gin import torch +import torch.distributed as dist from tqdm import tqdm from compass.residual_rl.constants import INPUT_IMAGE_SIZE @@ -42,9 +45,9 @@ def __init__(self, num_steps_per_env=100, ckpt_save_interval=50, debug_viz=False): - # Prepare log directory. - if not os.path.exists(output_dir): - os.makedirs(output_dir) + # Prepare log directory. exist_ok=True avoids a TOCTOU race when several + # torchrun ranks check + create concurrently. + os.makedirs(output_dir, exist_ok=True) self.output_dir = output_dir self.logger = logger # Init environment and base policy. @@ -53,6 +56,12 @@ def __init__(self, for param in self.base_policy.parameters(): param.requires_grad = False self.device = device + # Multi-rank state. WORLD_SIZE / RANK are set by torchrun; default to 1 / 0 in + # single-process runs. is_rank_zero gates writes (checkpoints, video upload, + # episode-log aggregation) so only rank 0 produces side-effects on disk. + self.world_size = int(os.environ.get("WORLD_SIZE", "1")) + self.global_rank = int(os.environ.get("RANK", "0")) + self.is_rank_zero = self.global_rank == 0 # Get policy and critic state dimensions. self.goal_heading_dim = 2 @@ -74,6 +83,11 @@ def __init__(self, actor_state_dim=self.policy_state_dim, action_dim=self.action_dim, critic_state_dim=self.critic_state_dim).to(self.device) + # Broadcast initial actor-critic params from rank 0 so all ranks start identical. + # Combined with PPO's gradient all-reduce, this keeps params synchronized across ranks. + if self.world_size > 1: + for p in actor_critic.parameters(): + dist.broadcast(p.data, src=0) # Init storage and model self.num_steps_per_env = num_steps_per_env @@ -91,6 +105,8 @@ def __init__(self, self.env.reset() + self._install_env_timers() + print(self) def __str__(self) -> str: @@ -139,6 +155,96 @@ def compute_states(self, policy_state, obs_dict, extras): self.critic_state_assembler.compute_critic_state(policy_state, obs_dict, extras) ] + def _install_env_timers(self): + # Monkey-patch a handful of Isaac Lab manager methods + sim.step / sim.render + # so each call adds its wall-clock time into self._env_breakdown. The trainer + # reads (and clears) this dict around every env.step() call to surface a + # per-stage breakdown of where env_step time goes. + # ~3% wall-clock overhead from extra cuda.synchronize() calls. + self._env_breakdown = {} + cuda_active = self.device != 'cpu' and torch.cuda.is_available() + env = self.env.unwrapped + targets = [ + ("action_pre", getattr(env, "action_manager", None), "process_action"), + ("termination", getattr(env, "termination_manager", None), "compute"), + ("reward", getattr(env, "reward_manager", None), "compute"), + ("command", getattr(env, "command_manager", None), "compute"), + ("event", getattr(env, "event_manager", None), "apply"), + ("obs", getattr(env, "observation_manager", None), "compute"), + ("reset_idx", env, "_reset_idx"), + ("sim_step", env.sim, "step"), + ("sim_render", env.sim, "render"), + ] + breakdown = self._env_breakdown + for key, obj, method_name in targets: + if obj is None or not hasattr(obj, method_name): + continue + original = getattr(obj, method_name) + + def _make_wrapper(orig, k): + + def wrapper(*args, **kwargs): + if cuda_active: + torch.cuda.synchronize() + t0 = time.perf_counter() + try: + return orig(*args, **kwargs) + finally: + if cuda_active: + torch.cuda.synchronize() + breakdown[k] = breakdown.get(k, 0.0) + (time.perf_counter() - t0) + + return wrapper + + setattr(obj, method_name, _make_wrapper(original, key)) + + # Drill one level deeper into observation_manager.compute by also wrapping + # each ObsTerm's func — surfaces which obs (camera_rgb_img, camera_depth_img, + # camera_to_world, route, ...) actually eats the time. + obs_mgr = getattr(env, "observation_manager", None) + if obs_mgr is not None: + # pylint: disable=protected-access — we need access to the obs_manager's + # internal term tables to wrap each obs term's func for perf measurement. + for group_name, term_cfgs in obs_mgr._group_obs_term_cfgs.items(): + term_names = obs_mgr._group_obs_term_names[group_name] + for name, cfg in zip(term_names, term_cfgs): + if not callable(getattr(cfg, "func", None)): + continue + original = cfg.func + key = f"obs_term/{group_name}/{name}" + + def _make_term_wrapper(orig, k): + + def wrapper(*args, **kwargs): + if cuda_active: + torch.cuda.synchronize() + t0 = time.perf_counter() + try: + return orig(*args, **kwargs) + finally: + if cuda_active: + torch.cuda.synchronize() + breakdown[k] = breakdown.get(k, 0.0) + (time.perf_counter() - t0) + + return wrapper + + cfg.func = _make_term_wrapper(original, key) + + @contextmanager + def _timer(self, key, accumulator): + # CUDA ops are async; sync so the timer covers the actual GPU work. + # Cumulative add lets the same key be used across rollout sub-steps. + cuda_active = self.device != 'cpu' and torch.cuda.is_available() + if cuda_active: + torch.cuda.synchronize() + t0 = time.perf_counter() + try: + yield + finally: + if cuda_active: + torch.cuda.synchronize() + accumulator[key] = accumulator.get(key, 0.0) + (time.perf_counter() - t0) + def learn(self, num_learning_iterations): self.train_mode() @@ -162,74 +268,96 @@ def learn(self, num_learning_iterations): print(10 * "_") print(f"Learning iteration {it}") print("Rollout:") + times = {"checkpoint": 0.0} with torch.inference_mode(): - self.env.reset() - # Get obs ready for policy state and base action from base_policy - obs_dict = self.env.unwrapped.observation_manager.compute() - policy_state, base_actions, history, sample, extras = self.base_policy_process( - obs_dict) - states = self.compute_states(policy_state, obs_dict, extras) - - for _ in tqdm(range(self.num_steps_per_env)): - # Get actions. - residual_actions = self.alg.act(states) - final_actions = base_actions + residual_actions - - # Run env step. - obs_dict, rewards, dones, truncateds, infos = self.env.step( - final_actions.float()) - - # Move time out information to the extras dict - # this is only needed for infinite horizon tasks - if not self.env.unwrapped.cfg.is_finite_horizon: - infos["time_outs"] = truncateds - dones = torch.cat([ - dones.reshape(self.env.unwrapped.num_envs, 1), - truncateds.reshape(self.env.unwrapped.num_envs, 1) - ], - dim=1) - dones = torch.any(dones, dim=1) - - # Process dones for ppo. - self.alg.process_env_step(rewards, dones) - - # Process dones for base policy and policy state. - history[dones] = 0 - sample[dones] = 0 - final_actions[dones] = 0 - - if "log" in infos: - ep_logs.append(infos["log"]) - - # Visualize actions. - if self.debug_viz: - self.env.unwrapped.action_manager.get_term('drive_joints').visualize( - base_actions, residual_actions) - - # Process obs for next step + with self._timer("env_reset_and_init", times): + self.env.reset() + # Get obs ready for policy state and base action from base_policy + obs_dict = self.env.unwrapped.observation_manager.compute() policy_state, base_actions, history, sample, extras = self.base_policy_process( - obs_dict, history, sample, final_actions) + obs_dict) states = self.compute_states(policy_state, obs_dict, extras) + # The four rollout/* sub-step timers below sum to slightly less than + # rollout total; the gap is small bookkeeping ops (dones reshape/cat, + # history zeroing, viz) which we don't time individually. + with self._timer("rollout", times): + for _ in tqdm(range(self.num_steps_per_env)): + # Get actions. + with self._timer("rollout/act", times): + residual_actions = self.alg.act(states) + final_actions = base_actions + residual_actions + + # Run env step. + self._env_breakdown.clear() + with self._timer("rollout/env_step", times): + obs_dict, rewards, dones, truncateds, infos = self.env.step( + final_actions.float()) + for _k, _v in self._env_breakdown.items(): + times[f"rollout/env_step/{_k}"] = times.get( + f"rollout/env_step/{_k}", 0.0) + _v + + # Move time out information to the extras dict + # this is only needed for infinite horizon tasks + if not self.env.unwrapped.cfg.is_finite_horizon: + infos["time_outs"] = truncateds + dones = torch.cat([ + dones.reshape(self.env.unwrapped.num_envs, 1), + truncateds.reshape(self.env.unwrapped.num_envs, 1) + ], + dim=1) + dones = torch.any(dones, dim=1) + + # Process dones for ppo. + with self._timer("rollout/process_env_step", times): + self.alg.process_env_step(rewards, dones) + + # Process dones for base policy and policy state. + history[dones] = 0 + sample[dones] = 0 + final_actions[dones] = 0 + + if "log" in infos: + ep_logs.append(infos["log"]) + + # Visualize actions. + if self.debug_viz: + self.env.unwrapped.action_manager.get_term('drive_joints').visualize( + base_actions, residual_actions) + + # Process obs for next step + with self._timer("rollout/base_policy_process", times): + (policy_state, base_actions, history, sample, + extras) = self.base_policy_process(obs_dict, history, sample, + final_actions) + states = self.compute_states(policy_state, obs_dict, extras) + # Learning step - self.alg.compute_returns(states) + with self._timer("compute_returns", times): + self.alg.compute_returns(states) print("Update") - mean_value_loss, mean_surrogate_loss = self.alg.update() + with self._timer("update", times): + mean_value_loss, mean_surrogate_loss, ppo_diagnostics = self.alg.update() self.logger.log_dict( { "Mean_value_loss": mean_value_loss, "Mean_surrogate_loss": mean_surrogate_loss, + **ppo_diagnostics, }, step=it) - self._save_episode_logs(ep_logs, it) - self._upload_video(it) - ep_logs.clear() + with self._timer("logging", times): + self._save_episode_logs(ep_logs, it) + self._upload_video(it) + ep_logs.clear() # Save checkpoint. if it % self.ckpt_save_interval == 0: - self._save_ckpt(os.path.join(self.output_dir, f"model_{it}.pt")) + with self._timer("checkpoint", times): + self._save_ckpt(os.path.join(self.output_dir, f"model_{it}.pt")) + + self.logger.log_dict({f"time/{k}": v for k, v in times.items()}, step=it) self.current_learning_iteration += num_learning_iterations self._save_ckpt(os.path.join(self.output_dir, @@ -358,21 +486,56 @@ def _record_data(self, obs_dict, policy_state, actions, data_to_record): data_to_record['goal_heading'].append(obs_dict["policy"]['goal_heading']) def _save_episode_logs(self, ep_logs, iteration): - for key in ep_logs[0]: - info_tensor = torch.tensor([], device=self.device) - ep_info = ep_logs[-1] - # handle scalar and zero dimensional tensor infos - if key not in ep_info: - continue - if not isinstance(ep_info[key], torch.Tensor): - ep_info[key] = torch.Tensor([ep_info[key]]) - if len(ep_info[key].shape) == 0: - ep_info[key] = ep_info[key].unsqueeze(0) - info_tensor = torch.cat((info_tensor, ep_info[key].to(self.device))) - value = torch.mean(info_tensor) - self.logger.log_dict({key: value}, step=iteration) + # Each rank contributes the latest episode-log dict from its rollout + # (or None if no episodes terminated this iter). Rank 0 then computes + # a per-key element-weighted mean across all ranks: total elements + # summed / total elements counted. This is a true cross-rank + # aggregation, weighted by the number of envs that contributed on + # each rank (no equal-weight-per-rank skew, no dropped logs when + # some ranks happen to have empty ep_logs). + # all_gather_object is structurally symmetric (every rank sends one + # object, every rank gets the full list back) so it can't deadlock + # on rank-asymmetric data shapes the way per-key all_reduce can. + # Move tensor values to CPU before the gather so we don't round-trip + # cuda tensors through pickle and so the rank-0 aggregation is pure + # CPU work. + local = None + if ep_logs: + local = { + k: (v.detach().cpu() if isinstance(v, torch.Tensor) else v) + for k, v in ep_logs[-1].items() + } + if self.world_size > 1: + gathered = [None] * self.world_size + dist.all_gather_object(gathered, local) + else: + gathered = [local] + if not self.is_rank_zero: + return + keys = set() + for d in gathered: + if d is not None: + keys.update(d.keys()) + for key in sorted(keys): + total = 0.0 + count = 0 + for d in gathered: + if d is None or key not in d: + continue + v = d[key] + if not isinstance(v, torch.Tensor): + v = torch.Tensor([v]) + if v.ndim == 0: + v = v.unsqueeze(0) + v = v.float() + total += float(v.sum().item()) + count += int(v.numel()) + if count > 0: + self.logger.log_dict({key: total / count}, step=iteration) def _save_ckpt(self, path, infos=None): + if not self.is_rank_zero: + return saved_dict = { "model_state_dict": self.alg.actor_critic.state_dict(), "optimizer_state_dict": self.alg.optimizer.state_dict(), @@ -383,6 +546,8 @@ def _save_ckpt(self, path, infos=None): self.logger.log_artifact(path, "rl_es_checkpoints", "model") def _upload_video(self, iteration): + if not self.is_rank_zero: + return # Video generation can be slower than this function call, so add one iteration # delay for video upload. target_iteration = iteration - 1 @@ -397,7 +562,11 @@ def _upload_video(self, iteration): step=target_iteration) def load(self, path, load_optimizer=True): - loaded_dict = torch.load(path, weights_only=False) + # map_location pins both model and optimizer tensors directly onto this + # rank's device. Without it every rank deserializes onto the original + # save device (cuda:0 from rank-0's checkpoint) before copying, which + # spikes GPU 0's memory by N x model size on resume. + loaded_dict = torch.load(path, weights_only=False, map_location=self.device) self.alg.actor_critic.load_state_dict(loaded_dict["model_state_dict"]) if load_optimizer: self.alg.optimizer.load_state_dict(loaded_dict["optimizer_state_dict"]) diff --git a/compass/rl_env/README.md b/compass/rl_env/README.md deleted file mode 100644 index f943180..0000000 --- a/compass/rl_env/README.md +++ /dev/null @@ -1,100 +0,0 @@ -# Isaac Lab Extension for Mobility Embodiment Specialist using Residual RL - -## Overview - -This directory contains Isaac Lab extension for mobility embodiment specialist with residual RL. - -## Installation - -1. First, install Isaac Lab v2.1.0 and Isaac Sim 4.5 by following the [Isaac Lab installation guide](https://isaac-sim.github.io/IsaacLab/v2.1.0/source/setup/installation/index.html). - - The repo has been tested in Isaac Lab v2.1.0, so follow the steps below to set the correct version: - ```bash - # Clone the Isaac Lab repository - git clone git@github.com:isaac-sim/IsaacLab.git - - # Switch to the correct version - cd IsaacLab - git fetch origin - git checkout v2.1.0 - - # Install Isaac Lab - ./isaaclab.sh --install - ``` - > **NOTE**: There might be error messages about RSL_RL not found during installation, we can safely ignore it as a custimized RSL_RL libary is used for residual RL training. - - -2. Install the mobility_es extension in this directory after Isaac Lab and Sim are installed: - - ```bash - # Set Isaac Lab path - export ISAACLAB_PATH= - - # Install the extension - ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -e exts/mobility_es - ``` - -## Usage - -### Running the Environment - -```bash -${ISAACLAB_PATH}/isaaclab.sh -p scripts/play.py --enable_cameras -``` - -### Adding New Embodiments - -To add a new robot embodiment, follow these steps: - -1. **Create Robot Configuration**: - - Follow Isaac Lab [instructions](https://isaac-sim.github.io/IsaacLab/main/source/how-to/write_articulation_cfg.html) to add a new robot articulation configuration in [exts/mobility_es/mobility_es/config/robots.py](exts/mobility_es/mobility_es/config/robots.py) - - Ensure proper joint configurations, collision properties, and physics parameters are set - -2. **Create Environment Configuration**: - - Create a new RL environment class that inherits from `GoalReachingEnvCfg` in [exts/mobility_es/mobility_es/config/env_cfg.py](exts/mobility_es/mobility_es/config/env_cfg.py) - - Override the following components as needed: - - | Component | Description | - |-----------|-------------| - | `scene.robot` | Set robot articulation configuration | - | `scene.camera` | Configure camera settings | - | `actions` | Define action item for the embodiment | - | `observations` | Configure RL observations | - | `events` | Configure RL events | - | `rewards` | Configure RL rewards | - | `terminations` | Configure RL terminations | - - > **NOTE**: Each embodiment should define its own action term as a low-level controller that maps velocity commands to joint positions. We have pre-defined action terms for different robot embodiments in [exts/mobility_es/mobility_es/mdp/action](exts/mobility_es/mobility_es/mdp/action) that can be re-configured for new embodiments. - -3. **Register and Verify Environment**: - - Import your new environment in the play.py script [scripts/play.py](scripts/play.py) - - Verify the environment is functional by running the play.py script with the new environment - - -3. **Examples of Existing Embodiments**: -- [H1 Humanoid Robot](exts/mobility_es/mobility_es/config/h1_env_cfg.py) -- [Carter Mobile Robot](exts/mobility_es/mobility_es/config/carter_env_cfg.py) -- [Spot Quadruped Robot](exts/mobility_es/mobility_es/config/spot_env_cfg.py) -- [G1 Humanoid Robot](exts/mobility_es/mobility_es/config/g1_env_cfg.py) -- [Digit Humanoid Robot](exts/mobility_es/mobility_es/config/digit_env_cfg.py) - - -### Adding New Scenes - -To add a new scene, follow these steps: - -1. **Create Scene Configuration**: - - Follow Isaac Lab [instructions](https://isaac-sim.github.io/IsaacLab/main/source/tutorials/02_scene/create_scene.html) to add a new scene configuration in [exts/mobility_es/mobility_es/config/environments.py](exts/mobility_es/mobility_es/config/environments.py), make sure to inherit from `EnvSceneAssetCfg` with proper parameters like pose_sample_range, env_spacing, replicate_physics, etc. - - -2. **Add Occupancy Map [Optional]**: - - We use occupancy map for collision-free pose sampling, while optional, we recommend adding the occupancy map for the new scene to improve sampling efficiency. - - For occupancy map generation, follow this [insturction](https://docs.omniverse.nvidia.com/isaacsim/latest/features/ext_omni_isaac_occupancy_map.html) to create the occupancy map with the new scene USD file in Isaac Sim. Make sure to rotate the occupancy map properly to match the scene orientation and set the origin as the top-left of the image in the occupancy_map.yaml file. - - ![Occupancy Map Example](../../images/omap_generation.png) - - - Add the occupancy_map.yaml path in `OMAP_PATHS` in [exts/mobility_es/mobility_es/config/environments.py](exts/mobility_es/mobility_es/config/environments.py) with key as the scene name and value as the occupancy map yaml path. - -3. **Register and Verify Scene**: - - Import your new scene in the play.py script [scripts/play.py](scripts/play.py) - - Verify the scene is functional by running the play.py script with the new scene by setting `env_cfg.scene.environment = MyNewSceneCfg()` diff --git a/compass/rl_env/exts/mobility_es/mobility_es/__init__.py b/compass/rl_env/exts/mobility_es/mobility_es/__init__.py index 9ce6247..ef95fbd 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/__init__.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/__init__.py @@ -12,7 +12,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """ Python module serving as a project/extension template. """ diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/__init__.py b/compass/rl_env/exts/mobility_es/mobility_es/config/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/__init__.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/carter_env_cfg.py b/compass/rl_env/exts/mobility_es/mobility_es/config/carter_env_cfg.py index 847c0ff..931cf25 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/carter_env_cfg.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/carter_env_cfg.py @@ -21,6 +21,7 @@ from isaaclab.managers import ObservationTermCfg as ObsTerm from isaaclab.managers import EventTermCfg as EventTerm from isaaclab.managers import SceneEntityCfg +from isaaclab.managers import ActionTermCfg from mobility_es.config import scene_assets from mobility_es.config import robots @@ -32,8 +33,7 @@ class CarterActionsCfg: """Action specifications for the MDP.""" - drive_joints = mdp.ActionTermCfg(class_type=NonHolonomicPerfectControlAction, - asset_name="robot") + drive_joints = ActionTermCfg(class_type=NonHolonomicPerfectControlAction, asset_name="robot") @configclass diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/digit_env_cfg.py b/compass/rl_env/exts/mobility_es/mobility_es/config/digit_env_cfg.py index df3cfdf..e79031f 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/digit_env_cfg.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/digit_env_cfg.py @@ -24,7 +24,7 @@ from isaaclab.managers import ObservationTermCfg as ObsTerm from isaaclab.managers import SceneEntityCfg from isaaclab.utils import configclass -from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise +from isaaclab.utils.noise import UniformNoiseCfg as Unoise from mobility_es.config import scene_assets from mobility_es.config.robots import digit, DIGIT_ACTUATED_JOINT_NAMES @@ -58,7 +58,7 @@ def __post_init__(self): self.scene.camera = scene_assets.camera.replace( prim_path="{ENV_REGEX_NS}/Robot/torso_base/front_cam") self.scene.camera.offset = CameraCfg.OffsetCfg(pos=(0.06, 0.0, -0.065), - rot=(-0.2705, 0.6532, -0.6532, 0.2705981), + rot=(0.6532, -0.6532, 0.2705981, -0.2705), convention="ros") self.actions = DigitActionsCfg() diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/env_cfg.py b/compass/rl_env/exts/mobility_es/mobility_es/config/env_cfg.py index 3c65a2a..5e7fad5 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/env_cfg.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/env_cfg.py @@ -27,7 +27,8 @@ from isaaclab.sensors import ContactSensorCfg from isaaclab.scene import InteractiveSceneCfg from isaaclab.utils import configclass -from isaaclab.utils.noise import AdditiveUniformNoiseCfg as Unoise +from isaaclab.utils.noise import UniformNoiseCfg as Unoise +from isaaclab_physx.physics import PhysxCfg from mobility_es.config import environments from mobility_es.config import scene_assets @@ -311,10 +312,10 @@ def __post_init__(self): # general settings self.decimation = 20 self.episode_length_s = EPISODE_LENGTH_S - self.rerender_on_reset = True + self.num_rerenders_on_reset = 1 # simulation settings self.sim.dt = 0.005 - self.sim.physx.bounce_threshold_velocity = 0.2 + self.sim.physics = PhysxCfg(bounce_threshold_velocity=0.2) # default friction material self.sim.physics_material.static_friction = 1.0 self.sim.physics_material.dynamic_friction = 1.0 diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py b/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py index 7d90eba..9af0bc0 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py @@ -84,7 +84,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/CombinedSingleRack", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['CombinedSingleRack'], @@ -107,7 +107,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/CombinedMultiRack", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['CombinedMultiRack'], @@ -130,7 +130,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/GalileoLab", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['GalileoLab'], @@ -149,7 +149,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/WarehouseSingleRack", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['WarehouseSingleRack'], @@ -167,7 +167,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/WarehouseMultiRack", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['WarehouseMultiRack'], @@ -190,7 +190,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/SimpleOffice", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['SimpleOffice'], @@ -209,7 +209,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/Hospital", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.UsdFileCfg( usd_path=USD_PATHS['Hospital'], @@ -233,7 +233,7 @@ class EnvSceneAssetCfg(AssetBaseCfg): prim_path="{ENV_REGEX_NS}/RandomEnvs", init_state=AssetBaseCfg.InitialStateCfg( pos=(0, 0, 0.01), - rot=(1.0, 0.0, 0.0, 0.0), + rot=(0.0, 0.0, 0.0, 1.0), ), spawn=sim_utils.MultiUsdFileCfg( usd_path=[USD_PATHS['SimpleOffice'], USD_PATHS['GalileoLab'], USD_PATHS['SimpleWarehouse']], diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/g1_env_cfg.py b/compass/rl_env/exts/mobility_es/mobility_es/config/g1_env_cfg.py index 76dd33d..ca2a782 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/g1_env_cfg.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/g1_env_cfg.py @@ -50,7 +50,7 @@ def __post_init__(self): self.scene.camera = scene_assets.camera.replace( prim_path="{ENV_REGEX_NS}/Robot/torso_link/front_cam") self.scene.camera.offset = CameraCfg.OffsetCfg(pos=(0.1, 0.0, 0.45), - rot=(-0.2705, 0.6532, -0.6532, 0.2705981), + rot=(0.6532, -0.6532, 0.2705981, -0.2705), convention="ros") self.actions = G1ActionsCfg() diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/h1_env_cfg.py b/compass/rl_env/exts/mobility_es/mobility_es/config/h1_env_cfg.py index 513d5b8..39b4123 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/h1_env_cfg.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/h1_env_cfg.py @@ -50,7 +50,7 @@ def __post_init__(self): self.scene.camera = scene_assets.camera.replace( prim_path="{ENV_REGEX_NS}/Robot/torso_link/front_cam") self.scene.camera.offset = CameraCfg.OffsetCfg(pos=(0.15, 0.0, 0.65), - rot=(-0.2705, 0.6532, -0.6532, 0.2705981), + rot=(0.6532, -0.6532, 0.2705981, -0.2705), convention="ros") self.actions = H1ActionsCfg() diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/robots.py b/compass/rl_env/exts/mobility_es/mobility_es/config/robots.py index 85f6dee..f268d0f 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/robots.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/robots.py @@ -40,7 +40,7 @@ ), init_state=ArticulationCfg.InitialStateCfg( pos=(0.0, 0.0, 0.02), - rot=(1, 0.0, 0.0, 0), + rot=(0.0, 0.0, 0.0, 1), joint_pos={ "joint_wheel_left": 0.0, "joint_wheel_right": 0.0, @@ -62,7 +62,7 @@ "joint_caster_left", "joint_caster_right", ], - velocity_limit=1000, + velocity_limit_sim=360.0, stiffness=0.0, damping=0.0, ), diff --git a/compass/rl_env/exts/mobility_es/mobility_es/config/scene_assets.py b/compass/rl_env/exts/mobility_es/mobility_es/config/scene_assets.py index 14022c7..b867cc7 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/config/scene_assets.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/config/scene_assets.py @@ -43,6 +43,6 @@ horizontal_aperture=20.955, clipping_range=(0.1, 20.0)), offset=CameraCfg.OffsetCfg(pos=(0.10434, 0.0, 0.37439), - rot=(0.5, -0.5, 0.5, -0.5), + rot=(-0.5, 0.5, -0.5, 0.5), convention="ros"), ) diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/__init__.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/__init__.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/__init__.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/__init__.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/action_visualization.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/action_visualization.py index c4da327..fb79895 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/action_visualization.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/action_visualization.py @@ -16,6 +16,7 @@ from __future__ import annotations import torch +import warp as wp import isaaclab.utils.math as math_utils from isaaclab.markers import VisualizationMarkers @@ -38,7 +39,7 @@ def visualize(self, base_action, residual_action): if not self._base_markers or not self._residual_markers: self._initialize_action_markers() # Visualize base action. - robot_pos_w = self.robot.data.root_pos_w.clone() + robot_pos_w = wp.to_torch(self.robot.data.root_pos_w).clone() robot_pos_w[:, 2] += ACTION_HEIGHT base_action_arrow_scale, base_action_arrow_quat = self._resolve_xy_velocity_to_arrow( base_action[:, :2]) @@ -78,7 +79,7 @@ def _resolve_xy_velocity_to_arrow( zeros = torch.zeros_like(heading_angle) arrow_quat = math_utils.quat_from_euler_xyz(zeros, zeros, heading_angle) # convert everything back from base to world frame - base_quat_w = self.robot.data.root_quat_w + base_quat_w = wp.to_torch(self.robot.data.root_quat_w) arrow_quat = math_utils.quat_mul(base_quat_w, arrow_quat) return arrow_scale, arrow_quat diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/non_holonomic_perfect_control_action.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/non_holonomic_perfect_control_action.py index 08dbdca..501d699 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/non_holonomic_perfect_control_action.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/action/non_holonomic_perfect_control_action.py @@ -16,10 +16,11 @@ from __future__ import annotations import torch +import warp as wp from collections.abc import Sequence from isaaclab.assets.articulation import Articulation -from isaaclab.envs.mdp.actions import ActionTerm, ActionTermCfg +from isaaclab.managers import ActionTerm, ActionTermCfg from mobility_es.mdp.action.action_visualization import ActionVisualizer from mobility_es.wrapper.env_wrapper import RLESEnvWrapper @@ -61,19 +62,16 @@ def process_actions(self, actions): self._processed_actions = self.raw_actions[:, (0, 5)] def apply_actions(self): - # Get current asset state - root_state = self._asset.data.root_state_w - # Extract linear_x and angular_z from actions linear_x = self.processed_actions[:, 0] angular_z = self.processed_actions[:, 1] - # Get current position and orientation quaternion - pos = root_state[:, :3] - quat = root_state[:, 3:7] + # Get current position and orientation quaternion (xyzw: x=0, y=1, z=2, w=3) + pos = wp.to_torch(self._asset.data.root_link_pos_w).clone() + quat = wp.to_torch(self._asset.data.root_link_quat_w) - # Convert quaternion to yaw angle - yaw = 2 * torch.atan2(quat[:, 3], quat[:, 0]) + # Convert quaternion to yaw angle (xyzw convention: x=0, y=1, z=2, w=3) + yaw = 2 * torch.atan2(quat[:, 2], quat[:, 3]) # Update yaw with angular velocity dt = self._env.physics_dt @@ -83,23 +81,24 @@ def apply_actions(self): pos[:, 0] += linear_x * torch.cos(new_yaw) * dt pos[:, 1] += linear_x * torch.sin(new_yaw) * dt - # Convert new yaw to quaternion (rotation around z-axis only) + # Convert new yaw to quaternion (rotation around z-axis only, xyzw convention) new_quat = torch.zeros_like(quat) - new_quat[:, 0] = torch.cos(new_yaw / 2) - new_quat[:, 3] = torch.sin(new_yaw / 2) - - # Update root state - root_state[:, :3] = pos - root_state[:, 3:7] = new_quat - root_state[:, 7:10] = torch.stack([ - linear_x * torch.cos(new_yaw), linear_x * torch.sin(new_yaw), - torch.zeros_like(linear_x) + new_quat[:, 2] = torch.sin(new_yaw / 2) # z component + new_quat[:, 3] = torch.cos(new_yaw / 2) # w component + + # Write new pose and velocity separately (root_state_w API removed in IsaacLab 3.0) + new_pose = torch.cat([pos, new_quat], dim=-1) + new_vel = torch.stack([ + linear_x * torch.cos(new_yaw), + linear_x * torch.sin(new_yaw), + torch.zeros_like(linear_x), + torch.zeros_like(angular_z), + torch.zeros_like(angular_z), + angular_z, ], - dim=1) - root_state[:, 10:13] = torch.stack( - [torch.zeros_like(angular_z), - torch.zeros_like(angular_z), angular_z], dim=1) - self._asset.write_root_state_to_sim(root_state) + dim=1) + self._asset.write_root_pose_to_sim_index(root_pose=new_pose) + self._asset.write_root_velocity_to_sim_index(root_velocity=new_vel) def reset(self, env_ids: Sequence[int] | None = None) -> None: self._raw_actions[env_ids] = 0.0 diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/__init__.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/__init__.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/uniform_collision_free_pose_command.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/uniform_collision_free_pose_command.py index 10c773f..444b9a7 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/uniform_collision_free_pose_command.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/command/uniform_collision_free_pose_command.py @@ -18,6 +18,7 @@ import random import torch +import warp as wp from typing import TYPE_CHECKING from collections.abc import Sequence @@ -29,7 +30,7 @@ from mobility_es.mdp.command.commands_cfg import UniformCollisionFreePose2dCommandCfg -class UniformCollisionFreePoseCommand(commands.pose_2d_command.UniformPose2dCommand): +class UniformCollisionFreePoseCommand(commands.UniformPose2dCommand): """Command generator that generates pose commands containing a 3-D position and heading. The command generator samples uniform 2D positions around the environment origin. It sets @@ -73,7 +74,8 @@ def _resample_command(self, env_ids: Sequence[int]): num_resample_trials = 0 while num_resample_trials < self.cfg.max_resample_trial: # Obtain origins for the robots - self.pos_command_w[resample_env_ids] = self.robot.data.root_pos_w[resample_env_ids] + self.pos_command_w[resample_env_ids] = wp.to_torch( + self.robot.data.root_pos_w)[resample_env_ids] # Offset the position command by the current root position r = torch.empty(len(resample_env_ids), device=self.device) # Randomize the position diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/events.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/events.py index 6026141..0349d27 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/events.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/events.py @@ -16,6 +16,7 @@ from __future__ import annotations import torch +import warp as wp import isaaclab.utils.math as math_utils from isaaclab.managers import SceneEntityCfg @@ -30,8 +31,9 @@ def sample_root_state_uniform(env: RLESEnvWrapper, asset_cfg: SceneEntityCfg = SceneEntityCfg("robot")): # extract the used quantities (to enable type-hinting) asset = env.scene[asset_cfg.name] - # get default root state - root_states = asset.data.default_root_state[env_ids].clone() + # get default root state (pose and velocity separately in IsaacLab 3.0) + default_root_pose = wp.to_torch(asset.data.default_root_pose)[env_ids].clone() + default_root_vel = wp.to_torch(asset.data.default_root_vel)[env_ids].clone() # Sample poses pose_range_list = [ @@ -43,10 +45,10 @@ def sample_root_state_uniform(env: RLESEnvWrapper, rand_samples = math_utils.sample_uniform(pose_ranges[:, 0], pose_ranges[:, 1], (len(env_ids), 6), device=asset.device) - positions = root_states[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] + positions = default_root_pose[:, 0:3] + env.scene.env_origins[env_ids] + rand_samples[:, 0:3] orientations_delta = math_utils.quat_from_euler_xyz(rand_samples[:, 3], rand_samples[:, 4], rand_samples[:, 5]) - orientations = math_utils.quat_mul(root_states[:, 3:7], orientations_delta) + orientations = math_utils.quat_mul(default_root_pose[:, 3:7], orientations_delta) # Sample velocities velocity_range_list = [ @@ -56,7 +58,7 @@ def sample_root_state_uniform(env: RLESEnvWrapper, rand_samples = math_utils.sample_uniform(velocity_ranges[:, 0], velocity_ranges[:, 1], (len(env_ids), 6), device=asset.device) - velocities = root_states[:, 7:13] + rand_samples + velocities = default_root_vel + rand_samples return positions, orientations, velocities diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/observations.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/observations.py index aa517b6..fc9b86b 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/observations.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/observations.py @@ -16,6 +16,7 @@ from __future__ import annotations import torch +import warp as wp from isaaclab.assets import RigidObject from isaaclab.managers import SceneEntityCfg @@ -31,7 +32,7 @@ def root_speed( ) -> torch.Tensor: """The speed of the root, which is the normalization of the linear velocity""" asset: RigidObject = env.scene[asset_cfg.name] - return torch.norm(asset.data.root_lin_vel_w, dim=1).unsqueeze(1) + return torch.norm(wp.to_torch(asset.data.root_lin_vel_w), dim=1).unsqueeze(1) def root_yaw_rate( @@ -40,7 +41,7 @@ def root_yaw_rate( ) -> torch.Tensor: """The yaw rate of the root""" asset: RigidObject = env.scene[asset_cfg.name] - return asset.data.root_ang_vel_w[:, 2].unsqueeze(1) + return wp.to_torch(asset.data.root_ang_vel_w)[:, 2].unsqueeze(1) def camera_img(env: RLESEnvWrapper, data_type: str = "rgb"): @@ -68,8 +69,8 @@ def camera_to_world(env: RLESEnvWrapper): batch_size = pos_w.shape[0] R_w_c = torch.zeros((batch_size, 3, 3), device=pos_w.device) - # Extract quaternion components - w, x, y, z = quat_w_world[:, 0], quat_w_world[:, 1], quat_w_world[:, 2], quat_w_world[:, 3] + # Extract quaternion components (xyzw convention: x=0, y=1, z=2, w=3) + x, y, z, w = quat_w_world[:, 0], quat_w_world[:, 1], quat_w_world[:, 2], quat_w_world[:, 3] # Fill rotation matrix using quaternion R_w_c[:, 0, 0] = 1 - 2 * (y**2 + z**2) @@ -153,7 +154,7 @@ def foot_print( ) -> torch.Tensor: """Represent the foot print of the asset as a rectangle that is offset to the asset root.""" robot: RigidObject = env.scene[asset_cfg.name] - root_pos = robot.data.root_pos_w - env.scene.env_origins + root_pos = wp.to_torch(robot.data.root_pos_w) - env.scene.env_origins root_pos_2d = root_pos[:, :2] def create_footprint_tensor(length, width, device): diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/termination.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/termination.py index c18ef0e..1812c31 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/termination.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/termination.py @@ -16,6 +16,7 @@ from __future__ import annotations import torch +import warp as wp from isaaclab.assets import RigidObject from isaaclab.managers import SceneEntityCfg @@ -37,10 +38,10 @@ def goal_reached( robot: RigidObject = env.scene[robot_cfg.name] # Check if the robot velocity is below the threshold - root_linear_vel = torch.norm(robot.data.root_lin_vel_w, dim=1) - root_angular_vel = robot.data.root_ang_vel_w[:, 2].abs() - vel_threshold = torch.logical_and(root_linear_vel < linear_vel_threshold, - root_angular_vel < angular_vel_threshold) + root_linear_vel = torch.norm(wp.to_torch(robot.data.root_lin_vel_w), dim=1) + root_angular_vel = wp.to_torch(robot.data.root_ang_vel_w)[:, 2].abs() + vel_threshold = torch.logical_and(root_linear_vel < linear_vel_threshold, root_angular_vel + < angular_vel_threshold) # Check if the robot is close to the goal and has low velocity goal_pose = goal_pose_in_robot_frame(env, command_name, offset, robot_cfg) @@ -56,6 +57,6 @@ def goal_reached( def nan_pose(env: RLESEnvWrapper, robot_cfg: SceneEntityCfg = SceneEntityCfg("robot")): # Check if the robot pose is nan, which can happen when the robot explodes in simulation. - nan_pos = torch.isnan(env.scene[robot_cfg.name].data.root_pos_w) - nan_quat = torch.isnan(env.scene[robot_cfg.name].data.root_quat_w) + nan_pos = torch.isnan(wp.to_torch(env.scene[robot_cfg.name].data.root_pos_w)) + nan_quat = torch.isnan(wp.to_torch(env.scene[robot_cfg.name].data.root_quat_w)) return torch.logical_or(torch.any(nan_pos, dim=1), torch.any(nan_quat, dim=1)) diff --git a/compass/rl_env/exts/mobility_es/mobility_es/mdp/utils.py b/compass/rl_env/exts/mobility_es/mobility_es/mdp/utils.py index 15a0d92..db9a5cb 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/mdp/utils.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/mdp/utils.py @@ -16,6 +16,7 @@ from __future__ import annotations import torch +import warp as wp from typing import TYPE_CHECKING import isaaclab.utils.math as math_utils @@ -38,7 +39,7 @@ def goal_pose_in_robot_frame( # Compute offset. robot = env.scene[robot_cfg.name] - robot_yaw = math_utils.euler_xyz_from_quat(robot.data.root_quat_w)[2].reshape( + robot_yaw = math_utils.euler_xyz_from_quat(wp.to_torch(robot.data.root_quat_w))[2].reshape( (goal_pose.shape[0], 1)) offset_vec = torch.cat([ offset[0] * torch.cos(robot_yaw) - offset[1] * torch.sin(robot_yaw), diff --git a/compass/rl_env/exts/mobility_es/mobility_es/utils/__init__.py b/compass/rl_env/exts/mobility_es/mobility_es/utils/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/utils/__init__.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/utils/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py b/compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py index a0f0107..a864a5d 100644 --- a/compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py +++ b/compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py @@ -46,6 +46,19 @@ def __init__(self, cfg): yaml_file_path = val break + # Fallback: when OMAP_PATHS has no entry for the scene, look for an + # auto-generated map at /omap/occupancy_map.yaml — this is + # where scripts/generate_omap_from_usd.py emits its output by default. + # Skipped when the scene uses MultiUsdFileCfg (no single USD path). + if yaml_file_path is None: + spawn_cfg = getattr(cfg.environment, "spawn", None) + usd_path = getattr(spawn_cfg, "usd_path", None) if spawn_cfg is not None else None + if isinstance(usd_path, str) and usd_path: + candidate = os.path.join(os.path.dirname(usd_path), "omap", "occupancy_map.yaml") + if os.path.exists(candidate): + yaml_file_path = candidate + print(f"Auto-discovered omap sibling of USD: {candidate}") + if yaml_file_path and os.path.exists(yaml_file_path): print(f'Loaded omap for env {env_prim_path}') self._load_grid_map(yaml_file_path) diff --git a/compass/rl_env/exts/mobility_es/pyproject.toml b/compass/rl_env/exts/mobility_es/pyproject.toml new file mode 100644 index 0000000..8d32633 --- /dev/null +++ b/compass/rl_env/exts/mobility_es/pyproject.toml @@ -0,0 +1,3 @@ +[build-system] +requires = ["setuptools>=45", "wheel", "toml"] +build-backend = "setuptools.build_meta" diff --git a/compass/rl_env/exts/mobility_es/setup.py b/compass/rl_env/exts/mobility_es/setup.py index 20f6343..f320a04 100644 --- a/compass/rl_env/exts/mobility_es/setup.py +++ b/compass/rl_env/exts/mobility_es/setup.py @@ -12,7 +12,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """Installation script for the 'mobility_es' python package.""" import os diff --git a/compass/rl_env/scripts/__init__.py b/compass/rl_env/scripts/__init__.py index 7ab23ee..3159bfe 100644 --- a/compass/rl_env/scripts/__init__.py +++ b/compass/rl_env/scripts/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/compass/utils/logger.py b/compass/utils/logger.py index b53a428..f9c3904 100644 --- a/compass/utils/logger.py +++ b/compass/utils/logger.py @@ -12,7 +12,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """ Flexible logging system that supports both Weights & Biases and TensorBoard. """ diff --git a/dev_env_plan.md b/dev_env_plan.md new file mode 100644 index 0000000..7cb0d0c --- /dev/null +++ b/dev_env_plan.md @@ -0,0 +1,177 @@ +# Docker-as-venv dev environment plan + +> Quality-of-life follow-up to the COMPASS 2.0 release. Live shape of the work +> is in this file; tracker entry is in `release_tracker.md` § 7. + +## Context + +Pre-PR-7 first-run UX is **6–8 manual steps and ~30–60 min**: clone Isaac Lab, +checkout `v3.0.0-beta1`, `./isaaclab.sh --install`, set `ISAACLAB_PATH`, create +a venv, `pip install -r requirements.txt` through the wrapper, install the +X-Mobility wheel, download the X-Mobility checkpoint, download `compass_usds.zip` +and unzip. The bare-metal path also breaks on the wrong host Python / stale +Vulkan SDK. + +PR-1 (`liuw/isaaclab_3.0_migration`) already bakes those installs into +`docker/Dockerfile.rl`, so the *image* is one-step ready. PR-7 fills in the +host-facing UX. + +**Goal:** cut first-run UX to **"3 commands, ~3 min after the image build"** +**and** make the steady-state dev loop feel like a Python venv — host-side +editor, host-side shell, but every `python`/`pip`/`tensorboard` invocation +transparently routed through the container. + +## Three-layer dev model + +``` +Host shell ──► shim PATH ──► docker exec ──► daemon container +(your editor, (auto-set by (compass-rl image, + your terminal, `source docker/activate`) repo bind-mounted at + your prompt) /workspace/COMPASS) +``` + +`source docker/activate` brings up a long-running daemon container if needed, +prepends a tmp shim dir to `PATH`, and rewrites `PS1` to show `(compass-rl)`. +The shims (`python`, `pip`, `isaaclab.sh`, `tensorboard`, `pytest`, `yapf`, +`pylint`, `pre-commit`) each `docker exec` the same-named binary inside the +container, with the host CWD translated to the container path. `deactivate` +reverts PATH/PS1 and removes the shim dir; the container keeps running until +`./docker/run.sh down`. + +## Layout + +``` +COMPASS/ +├── docker/ +│ ├── Dockerfile.rl # +5 lines vs PR-1: WORKDIR /workspace/COMPASS, python wrapper +│ ├── Dockerfile.distillation +│ ├── run.sh # build/assets/up/down/exec/shell/status +│ ├── activate # source me; shim PATH + (compass-rl) prompt +│ ├── prepare_assets.sh # HF downloader (USDs + X-Mobility ckpt) +│ └── README.md # subcommand reference + troubleshooting +├── osmo/ # unchanged (uses the same image, different launcher) +└── README.md # Quick Start leads with ./docker/run.sh +``` + +Everything Docker-related lives under `docker/`. No new top-level dir, no +`run.sh`-vs-`run.py` confusion at the repo root. + +## Elegance: one bind-mount covers everything + +Single source of truth for mount + env args is `_compass_run_args()` inside +`docker/run.sh`. The complete list: + +```bash +-v "${REPO_ROOT}:/workspace/COMPASS" # repo (covers code + configs + ./assets/) +-v "/tmp/.X11-unix:/tmp/.X11-unix:rw" # X server socket +-v "${HOME}/.cache/compass/kit:/isaac-sim/kit/cache" # writable shader cache +-e HOME=/workspace/COMPASS # routes pip / pre-commit / hf caches into the bind-mount +-e DISPLAY=$DISPLAY +-e WANDB_API_KEY=$WANDB_API_KEY +-e HF_TOKEN=$HF_TOKEN +-e ACCEPT_EULA=Y +-e OMNI_KIT_ALLOW_ROOT=1 +--gpus all +--net=host +--user "$(id -u):$(id -g)" +--passwd-entry "$(id -un):x:$(id -u):$(id -g):COMPASS dev:/workspace/COMPASS:/bin/bash" +--workdir /workspace/COMPASS +``` + +That's it: **three bind-mounts, six env vars**. Compare to +`robotic_grounding/workflow/run.sh:101-218` which mounts ~10 paths individually +because it isn't structured around a single repo root. + +### Why `/workspace/COMPASS` instead of `/workspace` + +The base image (`nvcr.io/nvidia/isaac-lab:3.0.0-beta1`) already places Isaac Lab +at `/workspace/isaaclab`. Bind-mounting `$(pwd) → /workspace` would shadow it. +Mounting at `/workspace/COMPASS` keeps `/workspace/isaaclab` accessible at its +expected `${ISAACLAB_PATH}` path while still putting the host repo at a stable +location inside the container. + +## Container naming + +```bash +COMPASS_CONTAINER="compass-$(id -un)-$(printf '%s' "${REPO_ROOT}" | sha1sum | cut -c1-8)" +``` + +Hash of the absolute repo path → multiple checkouts of COMPASS coexist without +colliding, and the same checkout always lands on the same container name. + +## `python` wrapper (Dockerfile.rl change) + +`isaaclab.sh` is a Python CLI (it `exec`s `python -c "from isaaclab.cli import cli; cli()" "$@"`), +so symlinking `python → isaaclab.sh` would put you inside the CLI rather than at +a Python prompt. Instead the Dockerfile installs a real wrapper: + +```dockerfile +RUN printf '#!/usr/bin/env bash\nexec "${ISAACLAB_PATH}/_isaac_sim/python.sh" "$@"\n' \ + > /usr/local/bin/python \ + && chmod +x /usr/local/bin/python \ + && ln -sf /usr/local/bin/python /usr/local/bin/python3 +``` + +Now `python run.py` inside the container resolves directly to Isaac Sim's +bundled `python.sh`, bypassing the CLI overhead and matching what +`isaaclab.sh -p` would have done. + +## Git workflow + +`git` itself runs **on the host** — no shim. Repo is on host, SSH/GPG keys are +on host, `git commit -s` (DCO) and `git push` work without extra plumbing. +File ownership stays consistent because the container runs as +`--user "$(id -u):$(id -g)"`. + +The wrinkle: pre-commit hooks (yapf, pylint, clang-format) live in the +container's Python, not on the host. So: + +- **Activate before committing.** `pre-commit` is in the shim list, so when + host `git commit -s` invokes it via PATH, the shim routes the hook into the + container. +- **Hook-env cache** lives at `./.cache/pre-commit/` (inside the bind-mount), + surviving `down` + `up`. `.cache/` is gitignored. + +## Files + +| Path | Status | Why | +|------|--------|-----| +| `docker/run.sh` | NEW | Subcommand wrapper around `docker {build,run,exec,rm}`; single source of truth for mount/env args | +| `docker/activate` | NEW | Sourced; shim PATH, PS1 prefix, `deactivate` | +| `docker/prepare_assets.sh` | NEW | HF downloader (USDs + X-Mobility ckpt) | +| `docker/README.md` | NEW | Subcommand reference + troubleshooting | +| `docker/Dockerfile.rl` | MODIFIED | `WORKDIR /workspace/COMPASS`; install paths under `/workspace/COMPASS`; `python`/`python3` wrapper | +| `.dockerignore` | MODIFIED | Add `./assets/`, `./.cache/`, `./.git/` | +| `.gitignore` | MODIFIED | Add `/assets/`, `/.cache/` | +| `README.md` | MODIFIED | Quick Start leads with `./docker/run.sh` + `source ./docker/activate`; bare-metal moves under "Manual install" | +| `release_tracker.md` | MODIFIED | Item #7 status flipped 🟡; checklist progress | + +## Branch + commit + +- Branch `liuw/dev_environment`, off `liuw/auto_omap_from_usd` (PR-5 head, latest + in the stack). +- Single squashed DCO-signed commit titled + "Add Docker-as-venv dev environment (`docker/run.sh` + `docker/activate`)". + +## Verification + +1. **`./docker/run.sh build`** — image builds; `docker images | grep compass-rl` shows the tag. +2. **`./docker/run.sh assets`** — fresh `./assets/` dir gets `usd/` and `x_mobility.ckpt`; second run is a no-op. +3. **`./docker/run.sh up`** — daemon container starts; `./docker/run.sh status` reports `running` with the path-hashed name. +4. **`source ./docker/activate`** — prompt becomes `(compass-rl) …`; `which python` resolves to the shim dir; `python -V` reports Isaac Sim's bundled Python. +5. **CWD translation** — `cd compass/rl_env/exts/mobility_es && python -c 'import os; print(os.getcwd())'` prints `/workspace/COMPASS/compass/rl_env/exts/mobility_es`. +6. **End-to-end smoke** — from the activated shell: + `python run.py -c configs/train_config.gin -o /tmp/out -b ./assets/x_mobility.ckpt --num_envs 1 --enable_camera --headless` reaches first PPO iteration. +7. **`deactivate`** — prompt + PATH revert; shim dir removed from `/tmp`. +8. **`./docker/run.sh down`** — container removed; `docker ps -a | grep compass-` returns nothing. +9. **OSMO compatibility** — `osmo/run_osmo.py train ... --image ` still works. +10. **Pre-commit clean (activated shell)** — `pre-commit run --files docker/run.sh docker/activate docker/prepare_assets.sh docker/README.md docker/Dockerfile.rl README.md`. +11. **Git commit signs cleanly** — `git commit -s -m "test" --allow-empty` from an activated shell completes; resulting commit shows DCO trailer. + +## Out of scope + +- Replacing `osmo/run_osmo.py` — different concern; same image. +- Removing the bare-metal install in `compass/rl_env/README.md` — kept for non-Docker users. +- `docker compose` — single container, single image; compose adds a dep without UX gain. +- VSCode `.devcontainer` — orthogonal; works alongside the activate model. +- Pushing branches / opening PRs — held per current standing instruction. diff --git a/distillation_train.py b/distillation_train.py index c5f30e0..9067bec 100644 --- a/distillation_train.py +++ b/distillation_train.py @@ -51,7 +51,7 @@ def parse_arguments(task_mode): parser.add_argument('--wandb-project-name', '-n', type=str, - default='afm_rl_enhance_distillation', + required=True, help='The project name of W&B.') parser.add_argument('--wandb-run-name', '-r', diff --git a/docker/Dockerfile.rl b/docker/Dockerfile.rl index 04b3166..77f71c8 100644 --- a/docker/Dockerfile.rl +++ b/docker/Dockerfile.rl @@ -1,5 +1,26 @@ -FROM nvcr.io/nvidia/isaac-lab:2.1.0 +FROM nvcr.io/nvidia/isaac-lab:3.0.0-beta1 -WORKDIR /workspace +# Omniverse runtime env (required for any kit / pip step run as root in the container). +ENV ACCEPT_EULA=Y +ENV OMNI_KIT_ALLOW_ROOT=1 -COPY . /workspace +# COMPASS lives in /workspace/COMPASS so /workspace/isaaclab (from the base image) +# is preserved when docker/run.sh bind-mounts the host repo at runtime. +WORKDIR /workspace/COMPASS + +COPY . /workspace/COMPASS + +# Install COMPASS dependencies, the X-Mobility wheel, and the mobility_es Isaac Lab extension +# into Isaac Lab's bundled Python environment. +RUN ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -r /workspace/COMPASS/requirements.txt \ + && ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install /workspace/COMPASS/x_mobility/x_mobility-0.1.0-py3-none-any.whl \ + && ${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -e /workspace/COMPASS/compass/rl_env/exts/mobility_es + +# Provide a `python` / `python3` on PATH that runs Isaac Lab's bundled Python directly. +# `docker/activate` on the host shims host-side `python` to `docker exec ... python`, +# so a host-side `python run.py` ends up as Isaac Sim's python.sh executing run.py +# — no `${ISAACLAB_PATH}/isaaclab.sh -p` boilerplate at any call site. +RUN printf '#!/usr/bin/env bash\nexec "${ISAACLAB_PATH}/_isaac_sim/python.sh" "$@"\n' \ + > /usr/local/bin/python \ + && chmod +x /usr/local/bin/python \ + && ln -sf /usr/local/bin/python /usr/local/bin/python3 diff --git a/docker/activate b/docker/activate new file mode 100644 index 0000000..b4f25d6 --- /dev/null +++ b/docker/activate @@ -0,0 +1,132 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Source this file (don't execute it) for a Python-venv-like dev experience +# that runs commands inside the COMPASS docker container. +# +# source ./docker/activate # sets up shim PATH + (compass-rl) prompt prefix +# python run.py … # actually runs inside the container +# deactivate # revert +# +# What it does: +# 1. Ensures the container is running (./docker/run.sh up). +# 2. Generates a tmp dir of shim scripts for python / pip / etc. and prepends +# it to PATH. Each shim `docker exec`s the same-named binary inside the +# container, with the host CWD translated to the container path. +# 3. Adds a `(compass-rl) ` prefix to PS1 so you know you're activated. +# 4. Defines a `deactivate` function to revert (1)-(3) and clean up the shim dir. +# +# The container itself keeps running until you call `./docker/run.sh down`. +# `deactivate` only tears down the shim PATH; multiple shells can be activated +# against the same container concurrently. + +# Detect the directory containing this script. Works under bash (BASH_SOURCE) +# and zsh (use ${(%):-%N} fallback — sourced filename). +if [ -n "${BASH_SOURCE:-}" ]; then + _compass_activate_path="${BASH_SOURCE[0]}" +elif [ -n "${ZSH_VERSION:-}" ]; then + # shellcheck disable=SC2296 + _compass_activate_path="${(%):-%N}" +else + echo "[activate] Unsupported shell: source from bash or zsh" >&2 + return 1 2>/dev/null || exit 1 +fi +_COMPASS_REPO_ROOT="$(cd "$(dirname "${_compass_activate_path}")/.." && pwd)" +unset _compass_activate_path + +# Path the host repo is bind-mounted to inside the container — must match +# CONT_REPO_DIR in docker/run.sh. +_COMPASS_CONT_REPO_DIR="/workspace/COMPASS" + +# Pull container name + image tag from run.sh so the two stay in sync. +_COMPASS_CONTAINER="$( + REPO_ROOT="${_COMPASS_REPO_ROOT}" bash -c ' + cd "${REPO_ROOT}" + repo_hash=$(printf "%s" "${REPO_ROOT}" | sha1sum | cut -c1-8) + echo "compass-$(id -un)-${repo_hash}" + ' +)" + +# Bring the container up if it isn't already. +if ! "${_COMPASS_REPO_ROOT}/docker/run.sh" up; then + echo "[activate] Failed to bring up container; aborting." >&2 + unset _COMPASS_REPO_ROOT _COMPASS_CONTAINER + return 1 2>/dev/null || exit 1 +fi + +# Tmp shim dir, removed by `deactivate`. +_COMPASS_SHIM_DIR="$(mktemp -d "${TMPDIR:-/tmp}/compass-shims.XXXXXX")" + +# Shimmed commands. To add another tool, append it here — no other wiring needed. +_compass_shim_cmds=( + python python3 + pip pip3 + isaaclab.sh + tensorboard + pytest + yapf pylint + pre-commit +) + +# Generate one shim script per command. Each is a tiny bash wrapper that +# translates the host CWD into the container path before exec'ing. +for _cmd in "${_compass_shim_cmds[@]}"; do + cat > "${_COMPASS_SHIM_DIR}/${_cmd}" <&2; } +step() { echo -e "${BLUE}[STEP]${NC} $*"; } + +# ────────────────────────────────────────────────────────────────────────────── +# Defaults. +# ────────────────────────────────────────────────────────────────────────────── +REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +CACHE_DIR="${REPO_ROOT}/assets" +HF_TOKEN_ARG="" +FORCE=false + +USDS_URL="https://huggingface.co/nvidia/COMPASS/resolve/main/compass_usds.zip" +USDS_ZIP="${CACHE_DIR}/compass_usds.zip" +USDS_DIR="${CACHE_DIR}/usd" + +CKPT_URL="https://huggingface.co/nvidia/X-Mobility/resolve/main/x_mobility-nav2-semantic_action_path.ckpt" +CKPT_FILE="${CACHE_DIR}/x_mobility.ckpt" + +# ────────────────────────────────────────────────────────────────────────────── +# Argument parsing. +# ────────────────────────────────────────────────────────────────────────────── +usage() { + sed -n '5,15p' "${BASH_SOURCE[0]}" | sed 's/^# \?//' + exit "${1:-0}" +} +while [ "$#" -gt 0 ]; do + case "$1" in + --hf-token) HF_TOKEN_ARG="$2"; shift 2 ;; + --cache-dir) + CACHE_DIR="$2" + USDS_ZIP="${CACHE_DIR}/compass_usds.zip" + USDS_DIR="${CACHE_DIR}/usd" + CKPT_FILE="${CACHE_DIR}/x_mobility.ckpt" + shift 2 + ;; + --force) FORCE=true; shift ;; + -h|--help) usage 0 ;; + *) error "Unknown flag: $1"; usage 2 ;; + esac +done + +HF_TOKEN_EFFECTIVE="${HF_TOKEN_ARG:-${HF_TOKEN:-}}" + +# ────────────────────────────────────────────────────────────────────────────── +# Steps. +# ────────────────────────────────────────────────────────────────────────────── +check_hf_token() { + if [ -z "${HF_TOKEN_EFFECTIVE}" ]; then + error "HF_TOKEN not set. The COMPASS HuggingFace repo is gated." + error "" + error "To fix:" + error " 1. Request access at https://huggingface.co/nvidia/COMPASS" + error " 2. Get a token at https://huggingface.co/settings/tokens" + error " 3. export HF_TOKEN=hf_xxx (or pass --hf-token hf_xxx)" + exit 1 + fi + info "HuggingFace token found." +} + +check_downloader() { + if command -v curl >/dev/null 2>&1; then + DOWNLOADER=curl + elif command -v wget >/dev/null 2>&1; then + DOWNLOADER=wget + else + error "Neither curl nor wget is installed. Install one of them and re-run." + exit 1 + fi +} + +# Args: $1=URL $2=destination path +hf_download() { + local url="$1" dst="$2" + case "${DOWNLOADER}" in + curl) + curl -L --fail --progress-bar \ + -H "Authorization: Bearer ${HF_TOKEN_EFFECTIVE}" \ + -o "${dst}" "${url}" + ;; + wget) + wget --progress=bar:force \ + --header="Authorization: Bearer ${HF_TOKEN_EFFECTIVE}" \ + -O "${dst}" "${url}" + ;; + esac +} + +prepare_dirs() { + step "Creating ${CACHE_DIR}" + mkdir -p "${CACHE_DIR}" +} + +download_usds() { + if [ "${FORCE}" = true ]; then + rm -rf "${USDS_DIR}" "${USDS_ZIP}" + fi + if [ -d "${USDS_DIR}" ] && [ -n "$(ls -A "${USDS_DIR}" 2>/dev/null)" ]; then + info "USDs already extracted at ${USDS_DIR} — skipping." + return 0 + fi + + step "Downloading compass_usds.zip → ${USDS_ZIP}" + check_hf_token + hf_download "${USDS_URL}" "${USDS_ZIP}" + if [ ! -s "${USDS_ZIP}" ]; then + error "Downloaded compass_usds.zip is empty. Check your HF_TOKEN and access." + rm -f "${USDS_ZIP}" + exit 1 + fi + + step "Unzipping → ${USDS_DIR}" + if ! command -v unzip >/dev/null 2>&1; then + error "unzip is not installed. Install it (e.g. apt-get install unzip)." + exit 1 + fi + mkdir -p "${USDS_DIR}" + unzip -q -o "${USDS_ZIP}" -d "${USDS_DIR}" + info "USDs extracted to ${USDS_DIR}" +} + +download_ckpt() { + if [ "${FORCE}" = true ]; then rm -f "${CKPT_FILE}"; fi + if [ -s "${CKPT_FILE}" ]; then + info "X-Mobility checkpoint already present at ${CKPT_FILE} — skipping." + return 0 + fi + + step "Downloading X-Mobility ckpt → ${CKPT_FILE}" + # X-Mobility ckpt isn't always gated, but pass the token anyway — works in both cases. + if [ -z "${HF_TOKEN_EFFECTIVE}" ]; then + warn "HF_TOKEN not set; trying anonymous download (may fail if gated)." + fi + if [ -n "${HF_TOKEN_EFFECTIVE}" ]; then + hf_download "${CKPT_URL}" "${CKPT_FILE}" + else + case "${DOWNLOADER}" in + curl) curl -L --fail --progress-bar -o "${CKPT_FILE}" "${CKPT_URL}" ;; + wget) wget --progress=bar:force -O "${CKPT_FILE}" "${CKPT_URL}" ;; + esac + fi + if [ ! -s "${CKPT_FILE}" ]; then + error "Downloaded ckpt is empty. Check your HF_TOKEN and network." + rm -f "${CKPT_FILE}" + exit 1 + fi + info "Downloaded $(du -h "${CKPT_FILE}" | cut -f1) → ${CKPT_FILE}" +} + +show_summary() { + step "Asset summary" + echo "Cache dir: ${CACHE_DIR}" + if [ -d "${USDS_DIR}" ] && [ -n "$(ls -A "${USDS_DIR}" 2>/dev/null)" ]; then + echo " ✓ USDs: ${USDS_DIR} ($(du -sh "${USDS_DIR}" | cut -f1))" + else + echo " ✗ USDs: ${USDS_DIR} (missing)" + fi + if [ -s "${CKPT_FILE}" ]; then + echo " ✓ X-Mobility ckpt: ${CKPT_FILE} ($(du -h "${CKPT_FILE}" | cut -f1))" + else + echo " ✗ X-Mobility ckpt: ${CKPT_FILE} (missing)" + fi + echo "" + echo "These paths are bind-mounted into the container at:" + echo " /workspace/assets/usd" + echo " /workspace/assets/x_mobility.ckpt" +} + +main() { + info "COMPASS asset preparation" + info "=========================" + check_downloader + prepare_dirs + download_usds + download_ckpt + show_summary + info "Done." +} + +main "$@" diff --git a/docker/run.sh b/docker/run.sh new file mode 100755 index 0000000..a82472a --- /dev/null +++ b/docker/run.sh @@ -0,0 +1,224 @@ +#!/usr/bin/env bash +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Manage the COMPASS docker dev container. +# +# Usage: +# ./docker/run.sh build [tag] # build the image (default tag: latest) +# ./docker/run.sh assets [--hf-token TOK] # download USDs + X-Mobility ckpt to ./assets +# ./docker/run.sh up # idempotent: start daemon container if not running +# ./docker/run.sh down # docker rm -f the container +# ./docker/run.sh exec [...] # one-shot exec inside the running container +# ./docker/run.sh shell # escape hatch: drop into bash inside the container +# ./docker/run.sh status # show container + image state +# +# Companion: `source ./docker/activate` for a Python-venv-like dev experience. + +set -eu + +# Repo root: the dir containing this script's parent (docker/run.sh → COMPASS/). +REPO_ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" + +# Image tag. Override via `./docker/run.sh build mytag` then `COMPASS_IMAGE_TAG=mytag ./docker/run.sh up`. +IMAGE_TAG="${COMPASS_IMAGE_TAG:-latest}" +IMAGE_NAME="compass-rl:${IMAGE_TAG}" + +# Container name = "compass--<8-hex of repo path>". Hashing the absolute repo +# path means multiple checkouts of COMPASS coexist without colliding, and the same +# checkout always lands on the same container across shells / reboots. +_REPO_HASH="$(printf '%s' "${REPO_ROOT}" | sha1sum | cut -c1-8)" +CONTAINER_NAME="compass-$(id -un)-${_REPO_HASH}" + +# Writable shader cache lives outside the repo so a `git clean` doesn't nuke it. +KIT_CACHE_DIR="${HOME}/.cache/compass/kit" + +# ────────────────────────────────────────────────────────────────────────────── +# Output helpers (lifted from ros2_deployment/prepare_assets.sh). +# ────────────────────────────────────────────────────────────────────────────── +GREEN='\033[0;32m'; YELLOW='\033[1;33m'; RED='\033[0;31m'; BLUE='\033[0;34m'; NC='\033[0m' +info() { echo -e "${GREEN}[INFO]${NC} $*"; } +warn() { echo -e "${YELLOW}[WARNING]${NC} $*"; } +error() { echo -e "${RED}[ERROR]${NC} $*" >&2; } +step() { echo -e "${BLUE}[STEP]${NC} $*"; } + +# ────────────────────────────────────────────────────────────────────────────── +# Single source of truth for `docker run` flags. Used by `up`. +# Keep the mount list minimal — the repo bind-mount covers code + configs + +# assets, so we only add X11 socket forwarding and the kit shader cache. +# ────────────────────────────────────────────────────────────────────────────── +CONT_REPO_DIR="/workspace/COMPASS" + +_compass_run_args() { + mkdir -p "${KIT_CACHE_DIR}" + local args=( + --name "${CONTAINER_NAME}" + --gpus all + --net=host + --user "$(id -u):$(id -g)" + # Isaac Sim's install at /isaac-sim is mode drwxr-x--- with group + # `isaac-sim` (GID 1234 in the base image). Adding our host user to + # that group at runtime is the minimal way to grant access without + # modifying the base image. + --group-add 1234 + # Repo bind-mount overrides the COPY in Dockerfile.rl so host edits hot-reload. + # Path is /workspace/COMPASS to leave /workspace/isaaclab (Isaac Lab installation, + # from the base image) intact and accessible. + -v "${REPO_ROOT}:${CONT_REPO_DIR}" + -v "/tmp/.X11-unix:/tmp/.X11-unix:rw" + -v "${KIT_CACHE_DIR}:/isaac-sim/kit/cache" + # HOME=/workspace/COMPASS puts pip / pre-commit / huggingface caches under + # the bind-mount so they survive `down` + `up`. Caches land in ./.cache/ + # which is gitignored. + -e "HOME=${CONT_REPO_DIR}" + -e "DISPLAY=${DISPLAY:-}" + -e "WANDB_API_KEY=${WANDB_API_KEY:-}" + -e "HF_TOKEN=${HF_TOKEN:-}" + # Already ENV in Dockerfile.rl; redundant flags are harmless and explicit. + -e "ACCEPT_EULA=Y" + -e "OMNI_KIT_ALLOW_ROOT=1" + # Bind-mount host /etc/passwd + /etc/group read-only so the container + # resolves $(id -u)/$(id -g) to a real username. Without this, NSS + # lookups inside the container (huggingface_hub, getpass.getuser, …) + # fall over with "no passwd entry for uid …". + -v "/etc/passwd:/etc/passwd:ro" + -v "/etc/group:/etc/group:ro" + --workdir "${CONT_REPO_DIR}" + --detach + ) + printf '%s\n' "${args[@]}" +} + +# ────────────────────────────────────────────────────────────────────────────── +# Subcommands. +# ────────────────────────────────────────────────────────────────────────────── +cmd_build() { + local tag="${1:-${IMAGE_TAG}}" + step "Building ${IMAGE_NAME/:*/:${tag}} from docker/Dockerfile.rl" + cd "${REPO_ROOT}" + docker build --network=host -f docker/Dockerfile.rl -t "compass-rl:${tag}" . + info "Build complete: compass-rl:${tag}" +} + +cmd_assets() { + "${REPO_ROOT}/docker/prepare_assets.sh" "$@" +} + +cmd_up() { + if docker ps --format '{{.Names}}' | grep -qx "${CONTAINER_NAME}"; then + info "Container already running: ${CONTAINER_NAME}" + return 0 + fi + if docker ps -a --format '{{.Names}}' | grep -qx "${CONTAINER_NAME}"; then + # Stopped container with the same name — start it back up. + step "Restarting existing container: ${CONTAINER_NAME}" + docker start "${CONTAINER_NAME}" >/dev/null + info "Container running: ${CONTAINER_NAME}" + return 0 + fi + + if ! docker image inspect "${IMAGE_NAME}" >/dev/null 2>&1; then + error "Image ${IMAGE_NAME} not found. Run: ./docker/run.sh build" + exit 1 + fi + + step "Starting container: ${CONTAINER_NAME} (image: ${IMAGE_NAME})" + # Allow X11 connections from the docker user (no-op if no X server / xhost). + xhost +SI:localuser:"$(id -un)" >/dev/null 2>&1 || true + + # Override the image's entrypoint (Isaac Sim's runheadless.sh) with `sleep + # infinity` so the container stays alive as a passive shell host. We exec + # actual commands via `docker exec`. `--entrypoint` accepts only the + # executable; the image arg becomes the first positional argument. + local args=() + while IFS= read -r line; do args+=("$line"); done < <(_compass_run_args) + args+=(--entrypoint /bin/sleep) + docker run "${args[@]}" "${IMAGE_NAME}" infinity >/dev/null + info "Container running: ${CONTAINER_NAME}" +} + +cmd_down() { + if ! docker ps -a --format '{{.Names}}' | grep -qx "${CONTAINER_NAME}"; then + info "No container to stop: ${CONTAINER_NAME}" + return 0 + fi + step "Removing container: ${CONTAINER_NAME}" + docker rm -f "${CONTAINER_NAME}" >/dev/null + info "Container removed." +} + +cmd_exec() { + if [ "$#" -eq 0 ]; then + error "Usage: ./docker/run.sh exec [args...]" + exit 2 + fi + cmd_up >/dev/null # Lazy up: don't make the user remember. + # CWD translation: if the host CWD is inside the repo, mirror the same + # subdirectory inside the container so relative paths work. + local host_pwd cont_pwd + host_pwd="$(pwd)" + case "${host_pwd}" in + "${REPO_ROOT}"|"${REPO_ROOT}"/*) + cont_pwd="${CONT_REPO_DIR}${host_pwd#${REPO_ROOT}}" + ;; + *) + cont_pwd="${CONT_REPO_DIR}" + ;; + esac + # Add -t only when stdin AND stdout are a terminal — `docker exec -t` + # against a non-tty stdin errors with "cannot attach stdin to a TTY-enabled". + local exec_flags="-i" + if [ -t 0 ] && [ -t 1 ]; then exec_flags="-it"; fi + exec docker exec ${exec_flags} -w "${cont_pwd}" "${CONTAINER_NAME}" "$@" +} + +cmd_shell() { + cmd_up >/dev/null + local exec_flags="-i" + if [ -t 0 ] && [ -t 1 ]; then exec_flags="-it"; fi + exec docker exec ${exec_flags} -w "${CONT_REPO_DIR}" "${CONTAINER_NAME}" bash +} + +cmd_status() { + echo "Image: ${IMAGE_NAME}" + if docker image inspect "${IMAGE_NAME}" >/dev/null 2>&1; then + echo "Image OK: $(docker image inspect "${IMAGE_NAME}" --format '{{.Id}} ({{.Created}})')" + else + echo "Image OK: (not built — run ./docker/run.sh build)" + fi + echo "Container: ${CONTAINER_NAME}" + if docker ps --format '{{.Names}}' | grep -qx "${CONTAINER_NAME}"; then + echo "State: running" + elif docker ps -a --format '{{.Names}}' | grep -qx "${CONTAINER_NAME}"; then + echo "State: stopped" + else + echo "State: absent" + fi + echo "Repo root: ${REPO_ROOT}" + echo "Kit cache: ${KIT_CACHE_DIR}" +} + +usage() { + sed -n '5,16p' "${BASH_SOURCE[0]}" | sed 's/^# \?//' + exit "${1:-0}" +} + +# ────────────────────────────────────────────────────────────────────────────── +# Dispatch. +# ────────────────────────────────────────────────────────────────────────────── +if [ "$#" -eq 0 ]; then usage 2; fi +SUB="$1"; shift +case "${SUB}" in + build) cmd_build "$@" ;; + assets) cmd_assets "$@" ;; + up) cmd_up "$@" ;; + down) cmd_down "$@" ;; + exec) cmd_exec "$@" ;; + shell) cmd_shell "$@" ;; + status) cmd_status "$@" ;; + -h|--help|help) usage 0 ;; + *) + error "Unknown subcommand: ${SUB}" + usage 2 + ;; +esac diff --git a/docs/README.md b/docs/README.md new file mode 100644 index 0000000..6320093 --- /dev/null +++ b/docs/README.md @@ -0,0 +1,61 @@ +# COMPASS docs + +Source for everything published at **https://nvlabs.github.io/COMPASS/**. + +``` +docs/ +├── project_page/ # academic landing page (Bulma; served at /) +└── handbook/ # Sphinx + nvidia-sphinx-theme handbook (served at /docs/) +``` + +## Local preview + +From the repo root, build the handbook and serve it together with the +academic landing — same layout as production: + +```bash +pip install -r docs/handbook/requirements.txt +make -C docs/handbook html + +mkdir -p /tmp/_site +cp -r docs/project_page/. /tmp/_site/ +cp -r docs/handbook/_build/html /tmp/_site/docs + +python -m http.server -d /tmp/_site 8765 +# / → academic landing +# /docs/ → handbook +``` + +For live-reload while editing handbook pages (handbook only; no academic +landing): + +```bash +sphinx-autobuild docs/handbook docs/handbook/_build/html --port 8000 +``` + +## Deploy + +Automatic on every push to `main` that touches `docs/**` or +`.github/workflows/docs.yml`. The workflow at +[`.github/workflows/docs.yml`](../.github/workflows/docs.yml) installs +`docs/handbook/requirements.txt`, runs `make html` (which uses +`sphinx-build -W` so any warning fails the build), stages the academic +landing at site root, copies the handbook under `/docs/`, and deploys via +`actions/deploy-pages@v4` — no intermediate `gh-pages` branch. + +The legacy `gh_page` branch is a frozen archive only; not built, not deployed. + +## Editing tips + +- The handbook is **self-contained** — every page owns its content directly. + No `{include}` transclusions today, so editing a topic means editing one + file under `docs/handbook/`. +- Each handbook page has an "Edit on GitHub" link in the right-hand sidebar + that deep-links to the corresponding markdown source. The `edit_uri` in + [`handbook/conf.py`](handbook/conf.py) makes this work. +- `-W` strict builds catch broken internal links and unresolved + cross-references. Preview locally before pushing if you're touching nav + structure or relative links. +- New pages must be referenced from a `{toctree}` directive in + [`handbook/index.md`](handbook/index.md), otherwise Sphinx warns about + orphan pages. diff --git a/docs/handbook/Makefile b/docs/handbook/Makefile new file mode 100644 index 0000000..a2dadc6 --- /dev/null +++ b/docs/handbook/Makefile @@ -0,0 +1,15 @@ +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +.PHONY: help html clean + +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) + +html: + $(SPHINXBUILD) -W -b html "$(SOURCEDIR)" "$(BUILDDIR)/html" $(SPHINXOPTS) + +clean: + rm -rf $(BUILDDIR) diff --git a/docs/handbook/_static/custom.css b/docs/handbook/_static/custom.css new file mode 100644 index 0000000..3faae32 --- /dev/null +++ b/docs/handbook/_static/custom.css @@ -0,0 +1,49 @@ +/* SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. */ +/* SPDX-License-Identifier: Apache-2.0 */ +/* Light brand polish on top of nvidia-sphinx-theme. */ + +:root { + --compass-card-radius: 0.9rem; + --compass-card-shadow: 0 12px 32px rgba(0, 0, 0, 0.08); + --compass-card-border: rgba(127, 127, 127, 0.2); +} + +/* sphinx-design grid cards on the landing page */ +.sd-card { + border-radius: var(--compass-card-radius); + border: 1px solid var(--compass-card-border); + box-shadow: var(--compass-card-shadow); + height: 100%; +} + +.sd-card .sd-card-body { + padding: 1.15rem 1.2rem; +} + +.sd-card .sd-card-title { + margin-bottom: 0.45rem; +} + +/* Hero lead under the H1 on the landing */ +.hero-lead { + font-size: 1.1rem; + line-height: 1.6; + max-width: 66rem; + margin-bottom: 1.25rem; +} + +/* Inline pill chips for "Quick start", "Training", etc. */ +.workflow-pills { + margin: 1rem 0 1.4rem; +} + +.workflow-pill { + display: inline-block; + margin: 0 0.5rem 0.5rem 0; + padding: 0.35rem 0.7rem; + border-radius: 999px; + background: rgba(118, 185, 0, 0.12); /* NVIDIA green tint */ + color: inherit; + font-size: 0.95rem; + font-weight: 600; +} diff --git a/docs/handbook/agentic.md b/docs/handbook/agentic.md new file mode 100644 index 0000000..44c9315 --- /dev/null +++ b/docs/handbook/agentic.md @@ -0,0 +1,125 @@ +# Agentic skills + +COMPASS ships four [Claude Code](https://claude.com/claude-code) skills that +take you from a natural-language prompt to a runnable, deployed policy. +Each skill is a focused recipe the agent follows on your behalf. + +## Pick the right skill + +| If you want to… | Use this skill | Lives at | +|---|---|---| +| Train a policy, search SAGE-10k scenes, register a new scene, evaluate a checkpoint, submit to OSMO | [`/compass`](https://github.com/NVlabs/COMPASS/blob/main/.claude/skills/compass/SKILL.md) | umbrella | +| Deploy a TRAINED checkpoint to a robot — ONNX → TensorRT → ROS2 launch | [`/compass-deploy`](https://github.com/NVlabs/COMPASS/blob/main/.claude/skills/compass-deploy/SKILL.md) | specialty | +| Diagnose vague COMPASS errors — "training won't start", "what's wrong" | [`/compass-debug`](https://github.com/NVlabs/COMPASS/blob/main/.claude/skills/compass-debug/SKILL.md) | specialty | +| Onboard a new robot platform (cfg files, EmbodimentEnvCfgMap registration) | [`/compass-newembodiment`](https://github.com/NVlabs/COMPASS/blob/main/.claude/skills/compass-newembodiment/SKILL.md) | specialty | + +`/compass` is the default front door — if you're not sure, start there +and it'll point you at the matching specialty when your intent is +deploy / debug / onboarding. + +## How a skill gets invoked + +Inside a Claude Code session at the COMPASS repo root: + +``` +/skill compass +/skill compass-deploy deploy to a Carter +/skill compass-debug +/skill compass-newembodiment add Galileo robot, USD at ./assets/usd/galileo.usd, like Spot +``` + +Or just describe the task in plain English; Claude Code auto-routes when +the intent matches the skill's description. The four descriptions are +written to be non-overlapping, so a deploy-flavored question routes to +`/compass-deploy` even without the slash command. + +## What each skill does + +### `/compass` — umbrella front door + +End-to-end training pipeline. Covers: + +- **Scene search** against the SAGE-10k catalogue (`scripts/sage10k_search.py`). +- **SAGE → USD conversion** via `scripts/sage10k_to_usd.py`. +- **Scene registration** in + [`compass/rl_env/exts/mobility_es/mobility_es/config/environments.py`](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py) + and `EnvSceneAssetCfgMap` in `run.py`. +- **OMap generation** for the new scene (see [Auto OMap from USDs](omap.md)). +- **Training launch** via `run.py` — preview run with one env first, + then full-scale. +- **Evaluation** via `run.py` with `eval_config.gin`. +- **OSMO cloud submission** via [`osmo/run_osmo.py`](osmo.md). + +Auto-OMap is the SAGE-driven training smoothness win: SAGE scenes don't +ship with hand-authored occupancy maps, and the [auto OMap from +USDs](omap.md) generator closes that gap so every new scene the agent +registers comes with a ready-to-use OMap, no manual UI step. + +### `/compass-deploy` — checkpoint to robot + +Takes a trained checkpoint (`.pt`) and produces a runnable robot launch: + +1. ONNX export via `onnx_conversion.py` (specialist or generalist branch). +2. TensorRT engine build via `trt_conversion.py`. Engines are + GPU-architecture-specific — build on the same GPU class as the robot. +3. ROS2 launch scaffold pointing at the engine. Uses + `ros2_deployment/compass_navigator/launch/compass_navigator.launch.py` + with a `runtime_path:=` argument. +4. Smoke-test command for either Isaac Sim or real-robot bring-up. + +The skill produces the engine and launch command but does **not** run +`ros2 launch` itself — bringing up Isaac Sim or a real-robot stack +inside an agent session is too easy to derail. You run the launch from a +separate terminal where you can monitor and abort. + +For per-platform specifics (Carter / Spot / Galileo, sensor remapping, +NuRec real2sim), see [ROS2 Deployment](deployment/ros2.md). + +### `/compass-debug` — one-shot diagnostics + +Read-only health check. Bundles a `scripts/compass_status.sh` helper +that runs eight diagnostic checks in parallel and prints a markdown +table: + +| Check | What it verifies | +|---|---| +| Container | `compass-rl` is up | +| Activated shell | `./docker/activate` shim is on PATH | +| GPU | `nvidia-smi` responds | +| Base ckpt | `./assets/x_mobility.ckpt` exists | +| USDs | `./assets/usd/` has scene content | +| Recent log | a training run has produced log output | +| (--deep) Isaac Sim init | headless `SimulationApp` starts and closes | +| (--ckpt PATH) Ckpt load | `torch.load(...)` succeeds on the file | + +The skill reports the table, identifies the root cause, and points you +at the specialty skill that owns the fix — but does **not** auto-fix the +issue. Understanding the root cause is more useful than a silent +recovery. + +### `/compass-newembodiment` — onboard a new robot + +Interactive walkthrough of adding a new robot platform. Parses what you +already supplied (robot name, USD path, base-class hint) and only asks +about what's missing. + +1. Ask the four onboarding questions (skipping any you already answered). +2. Read the matching reference (`carter` for wheeled, `spot` for + quadrupeds, `h1` for humanoids). +3. Show a diff of the proposed changes — new `ArticulationCfg` block in + `robots.py`, new `_env_cfg.py`, edits to `run.py`. +4. Apply after your confirmation. +5. Smoke-test with `--num_envs 1` and the GUI on so you can watch the + robot spawn. + +The handbook page [Adding a new embodiment or scene](extending.md) is +the canonical written guide; the skill is the operational sibling. + +## Reading the skill source + +Skills are plain markdown with YAML frontmatter under `.claude/skills/`. +Each `SKILL.md` documents every sub-flow, the prompts the agent uses, +and the verification steps it runs. Treat it as the spec for what the +agent will do on your behalf — useful both for understanding what +happens and for editing the skill if you want to fork the workflow for +your own purposes. diff --git a/docs/handbook/conf.py b/docs/handbook/conf.py new file mode 100644 index 0000000..e725687 --- /dev/null +++ b/docs/handbook/conf.py @@ -0,0 +1,103 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +"""Sphinx configuration for the COMPASS documentation site.""" + +from __future__ import annotations + +import os +import sys + +# Make the repo root importable so future autodoc / autosummary can find compass.*. +ROOT = os.path.abspath("../..") +sys.path.insert(0, ROOT) + +# --------------------------------------------------------------------------- +# Project metadata +# --------------------------------------------------------------------------- + +project = "COMPASS" +copyright = "2025-2026, NVIDIA" +author = "NVIDIA" + +# Bump alongside CHANGELOG.md when we cut a release. +COMPASS_VERSION = "1.6.0" +version = COMPASS_VERSION +release = COMPASS_VERSION + +# --------------------------------------------------------------------------- +# Extensions +# --------------------------------------------------------------------------- + +extensions = [ + "myst_parser", + "sphinx.ext.napoleon", + "sphinx.ext.viewcode", + "sphinx.ext.intersphinx", + "sphinx.ext.todo", + "sphinx.ext.githubpages", + "sphinx_design", + "sphinx_copybutton", + "sphinxcontrib.mermaid", +] + +exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", ".venv", "venv"] + +# --------------------------------------------------------------------------- +# MyST (markdown) configuration +# --------------------------------------------------------------------------- + +myst_enable_extensions = [ + "colon_fence", # ::: directive blocks + "deflist", # definition lists + "fieldlist", # field lists + "tasklist", # GitHub-style task lists + "attrs_inline", # {.class} inline attribute syntax +] +myst_fence_as_directive = {"mermaid"} +myst_heading_anchors = 3 + +# The handbook is self-contained — every page owns its content directly. +# We deliberately do NOT suppress `myst.xref_missing` or `image.not_readable` +# so `-W` catches broken intra-handbook links and missing images. If a future +# {include} of an external README needs those suppressions back, scope them +# to that page rather than globally. + +# --------------------------------------------------------------------------- +# Intersphinx +# --------------------------------------------------------------------------- + +intersphinx_mapping = { + "python": ("https://docs.python.org/3", None), +} + +# --------------------------------------------------------------------------- +# Theme: nvidia-sphinx-theme +# https://github.com/NVIDIA/nvidia-sphinx-theme +# --------------------------------------------------------------------------- + +html_theme = "nvidia_sphinx_theme" +html_title = f"COMPASS Handbook" +html_show_sphinx = False +html_theme_options = { + "github_url": "https://github.com/NVlabs/COMPASS", + "copyright_override": { + "start": 2025 + }, + "pygments_light_style": "tango", + "pygments_dark_style": "monokai", +} + +html_static_path = ["_static"] +html_css_files = ["custom.css"] + +# --------------------------------------------------------------------------- +# Mermaid (sequence/flow diagrams in markdown) +# --------------------------------------------------------------------------- + +mermaid_version = "11" + +# --------------------------------------------------------------------------- +# Misc +# --------------------------------------------------------------------------- + +todo_include_todos = False diff --git a/docs/handbook/contributing.md b/docs/handbook/contributing.md new file mode 100644 index 0000000..4111b13 --- /dev/null +++ b/docs/handbook/contributing.md @@ -0,0 +1,31 @@ +# Contributing + +The contribution rules — sign-off, commit policy, pre-commit, code style — +live in +[`CONTRIBUTING.md`](https://github.com/NVlabs/COMPASS/blob/main/CONTRIBUTING.md): + +```{include} ../../CONTRIBUTING.md +``` + +## Editing this handbook + +Each page in this site has an "Edit on GitHub" button (top right) that +deep-links to the corresponding markdown source under +`docs/handbook/`. Edit, push, and the GitHub Actions workflow at +[`.github/workflows/docs.yml`](https://github.com/NVlabs/COMPASS/blob/main/.github/workflows/docs.yml) +will re-build and re-deploy automatically on push to `main`. + +To preview locally: + +```bash +pip install -r docs/handbook/requirements.txt +cd docs/handbook && make html +# Built to _build/html/ — open with any static server: +python -m http.server -d _build/html 8000 + +# Or use sphinx-autobuild for live reload while editing: +sphinx-autobuild . _build/html --port 8000 +``` + +The handbook builds with `-W` (warnings → errors), so any broken internal +link or unresolved reference fails CI. diff --git a/docs/handbook/deployment/isaac_sim.md b/docs/handbook/deployment/isaac_sim.md new file mode 100644 index 0000000..564aec3 --- /dev/null +++ b/docs/handbook/deployment/isaac_sim.md @@ -0,0 +1,264 @@ +# Isaac Sim setup + +The deeper-dive companion to the [ROS2 Deployment](ros2.md) overview — +container build, ROS2 bridge configuration, and the Isaac Sim launch +recipe. + +## Overview + +Standalone ROS2 deployment for the COMPASS navigator with Isaac Sim +integration. + +### What's included + +[`ros2_deployment/compass_container.py`](https://github.com/NVlabs/COMPASS/blob/main/ros2_deployment/compass_container.py) +is the container management script. It: + +- Manages **two separate Docker containers** — COMPASS Navigator and Isaac Sim +- Controls container lifecycle: start, stop, enter, monitor both containers +- Handles TensorRT model conversion automatically +- Launches the Isaac Sim container with the ROS2 bridge +- Provides easy container entry and debugging + +[`ros2_deployment/docker/Dockerfile.deploy`](https://github.com/NVlabs/COMPASS/tree/main/ros2_deployment/docker) +creates a container with: + +- ROS2 Humble + CUDA 11.8 + TensorRT +- the COMPASS Navigator package and its dependencies +- a non-root user setup for development +- proper FastDDS configuration for inter-container communication + +## Quick setup & run + +### 1. Build the container + +```bash +export COMPASS_PATH=/path/to/compass +export HUGGINGFACE_TOKEN=hf_your_token_here +cd $COMPASS_PATH/ros2_deployment +./build_compass_docker.sh +``` + +### 2. Launch Isaac Sim (terminal 1) + +```bash +./compass_container.py launch_isaac_sim +``` + +- **In Isaac Sim GUI:** Go to `Windows` → `Examples` → `Robotics Examples` → `ROS2` → `Navigation`. +- **Load a robot scene** (e.g., Carter). +- **Press Play (▶️)** to start simulation. + +### 3. Launch COMPASS Navigator with GUI (terminal 2) + +```bash +./compass_container.py --rviz launch_nav +``` + +- Automatically converts ONNX → TensorRT (first time only) +- Builds the ROS2 workspace and launches the navigator +- Opens RViz2 with robot visualization + +### 3.b Navigate the robot + +- **In RViz2:** use the **2D Nav Goal** tool in the toolbar. +- **Click anywhere on the map** to set navigation targets. +- **Robot navigates autonomously** using AI-powered path planning. + +## 4. Alternative: headless mode (no GUI) + +### 4.a Launch COMPASS Navigator + +```bash +./compass_container.py launch_nav +``` + +- Runs the navigator without RViz2 GUI (saves resources). +- All ROS2 topics still available for external tools. + +### 4.b Send navigation goals + +```bash +# Enter the compass container +./compass_container.py enter + +# Inside container, publish a goal pose +ros2 topic pub --once /goal_pose geometry_msgs/msg/PoseStamped " +header: + frame_id: 'odom' +pose: + position: + x: 2.0 + y: 0.0 + z: 0.0 + orientation: + x: 0.0 + y: 0.0 + z: 0.0 + w: 1.0 +" + +# Monitor navigation progress +ros2 topic echo /cmd_vel --once # robot commands +ros2 topic echo /chassis/odom --once # robot position +# (or visualize in Isaac Sim) +``` + +## Container commands + +```bash +# COMPASS Navigator container lifecycle +./compass_container.py start # start headless (background) +./compass_container.py --rviz start # start with RViz GUI +./compass_container.py enter # enter running container +./compass_container.py stop # stop container +./compass_container.py logs # view logs +./compass_container.py status # status + +# Isaac Sim container (separate) +./compass_container.py launch_isaac_sim # interactive + +# Combined launch options +./compass_container.py launch_nav # quick headless launch +./compass_container.py --rviz launch_nav # quick launch with RViz GUI +./compass_container.py launch_nav --force-trt-conversion # rebuild TRT engine (headless) +./compass_container.py --rviz launch_nav --force-trt-conversion + +# Development +./compass_container.py enter # enter headless container +./compass_container.py --rviz enter # enter with RViz support + +# Troubleshooting +./compass_container.py debug_network_setup # debug ROS2 network setup +``` + +## Container lifecycle + +### COMPASS Navigator container + +- **Starts in detached mode** (runs in background). +- **Persists after `launch_nav` exits** — the container keeps running. +- **Runs ROS2 nodes and RViz2** inside the container. +- **Must be manually stopped** with `./compass_container.py stop`. + +```bash +./compass_container.py launch_nav +# Command exits, but the container + ROS2 nodes continue running. + +./compass_container.py status # check if still running +./compass_container.py enter # access running container +./compass_container.py stop # stop container +``` + +### Isaac Sim container + +- **Starts in interactive mode** (blocks the terminal). +- **Container exits when the Isaac Sim GUI closes**. +- **Auto-removes itself** when terminated. +- **Command blocks** until you close Isaac Sim. + +```bash +./compass_container.py launch_isaac_sim +# Command blocks, shows the Isaac Sim GUI. +# Close Isaac Sim → container exits → command exits. +# The container is automatically removed. +``` + +### Typical workflow + +```bash +# Terminal 1: start Isaac Sim (interactive, blocks) +./compass_container.py launch_isaac_sim + +# Terminal 2: start COMPASS (detached, returns immediately) +./compass_container.py launch_nav + +# Use both systems... + +# Terminal 1: close the Isaac Sim GUI (container auto-exits) +# Terminal 2: stop the COMPASS container +./compass_container.py stop +``` + +## Troubleshooting + +**No GUI / black screen** + +```bash +# X11 permissions are checked automatically when launching Isaac Sim. +# If launch fails with X11 errors, fix permissions: +xhost +local:docker +export DISPLAY=:0 +``` + +**Topics not visible between containers** + +```bash +# Verify ROS2 domain consistency +echo $ROS_DOMAIN_ID # Should be the same in both containers (0 by default) +``` + +**Package not found errors** + +```bash +# Force a workspace rebuild +./compass_container.py enter +# Inside the container: +rm -rf build install log && colcon build --symlink-install +``` + +**Model updated (need TensorRT conversion)** + +```bash +# If you manually changed the ONNX model in assets/models/ +./compass_container.py launch_nav --force-trt-conversion +``` + +**Manual workspace build (inside container)** + +```bash +cd /home/compassuser/compass_ws +source /opt/ros/humble/setup.bash +colcon build --symlink-install +``` + +**Build issues — `HUGGINGFACE_TOKEN` not set** + +```bash +# Get a token from https://huggingface.co/settings/tokens after requesting +# access to nvidia/COMPASS. +export HUGGINGFACE_TOKEN=hf_your_token_here +./build_compass_docker.sh +``` + +**TensorRT conversion fails** + +```bash +# Check GPU memory usage +nvidia-smi +# Stop other GPU processes if needed +./compass_container.py stop +./compass_container.py launch_nav --force-trt-conversion +``` + +**ROS2 communication issues — robot doesn't respond to navigation commands** + +```bash +# Verify Isaac Sim is running and the scene is playing (Play ▶️ pressed). +# Inside the COMPASS Navigator container: +./compass_container.py enter +# Inside the container: +ros2 topic list | grep -E "(cmd_vel|odom|image)" +ros2 topic echo /cmd_vel --once # should show navigation commands when goals are set +``` + +**Network troubleshooting** + +```bash +# Automated diagnostics (recommended) +./compass_container.py debug_network_setup +# This checks env vars, network mode, and ROS2 topic discovery in both containers. + +# Manual checks (if needed): +docker inspect compass-ros2-navigator | grep NetworkMode # Should show: "NetworkMode": "host" +``` diff --git a/docs/handbook/deployment/ros2.md b/docs/handbook/deployment/ros2.md new file mode 100644 index 0000000..a627ef6 --- /dev/null +++ b/docs/handbook/deployment/ros2.md @@ -0,0 +1,57 @@ +# ROS2 Deployment + +COMPASS ships ROS2 nodes that consume a TensorRT engine and emit velocity +commands. The same nodes cover three integration paths: + +- **Isaac Sim** — driving a simulated robot end-to-end via the ROS2 bridge. + The detailed setup is on the [Isaac Sim setup](isaac_sim.md) sub-page. +- **Sim2Real** — deploying the same `compass_inference` node directly on a + real robot, optionally pairing with visual SLAM (e.g. cuVSLAM) for state + estimation. +- **Object navigation** — wiring an object-localization module (e.g. + Locate3D) into the bundled `obj_target_generator` node so the robot can + approach named objects. + +The packages live under +[`ros2_deployment/`](https://github.com/NVlabs/COMPASS/tree/main/ros2_deployment). + +## Provided ROS2 nodes + +- **`compass_inference`** — consumes camera images, target poses, and robot + speed inputs, then outputs velocity commands by running TensorRT inference + with COMPASS engines. +- **`obj_target_generator`** — receives object localization bounding boxes + and generates navigation target poses for the COMPASS navigator. + +These nodes enable a variety of integration workflows; the three below are +the bundled examples. + +## 1. Isaac Sim integration + +The [`compass_navigator`](https://github.com/NVlabs/COMPASS/tree/main/ros2_deployment/compass_navigator) +package is fully compatible with [NVIDIA Isaac Sim](https://developer.nvidia.com/isaac-sim), +leveraging its robotics environments and ROS2 bridge for seamless simulation. + +For detailed setup instructions and a step-by-step Isaac Sim integration guide, +see the [Isaac Sim setup](isaac_sim.md) sub-page. + +## 2. Zero-shot sim2real transfer + +The `compass_inference` node can also be deployed directly on real robots, +enabling a seamless transition from simulation to real-world operation. By +integrating visual SLAM solutions such as +[cuVSLAM](https://nvidia-isaac-ros.github.io/concepts/visual_slam/cuvslam/index.html) +for robot state estimation, the COMPASS model can support zero-shot sim2real +transfer. + + + +## 3. Object navigation integration + +By integrating an object localization module (e.g., +[Locate3D](https://locate3d.atmeta.com/)), we can also enable object navigation +with COMPASS. The `obj_target_generator` node can convert localized object +bounding boxes into navigation goals, allowing the robot to autonomously +approach specified objects. + + diff --git a/docs/handbook/extending.md b/docs/handbook/extending.md new file mode 100644 index 0000000..b410ade --- /dev/null +++ b/docs/handbook/extending.md @@ -0,0 +1,103 @@ +# Adding a new embodiment or scene + +The Isaac-Lab extension at +[`compass/rl_env/exts/mobility_es/`](https://github.com/NVlabs/COMPASS/tree/main/compass/rl_env/exts/mobility_es) +defines the embodiments (H1, Carter, Spot, G1, Digit, …) and scenes COMPASS +trains on. Adding your own is a registration exercise — the gin configs and +`run.py` driver pick it up once the entry exists. + +## Adding new embodiments + +To add a new robot embodiment, follow these steps: + +### 1. Create robot configuration + +- Follow the Isaac Lab [instructions](https://isaac-sim.github.io/IsaacLab/main/source/how-to/write_articulation_cfg.html) + to add a new robot articulation configuration in + [`compass/rl_env/exts/mobility_es/mobility_es/config/robots.py`](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/robots.py). +- Ensure proper joint configurations, collision properties, and physics + parameters are set. + +### 2. Create environment configuration + +- Create a new RL environment class that inherits from `GoalReachingEnvCfg` + in + [`compass/rl_env/exts/mobility_es/mobility_es/config/env_cfg.py`](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/env_cfg.py). +- Override the following components as needed: + + | Component | Description | + |-----------|-------------| + | `scene.robot` | Set robot articulation configuration | + | `scene.camera` | Configure camera settings | + | `actions` | Define action item for the embodiment | + | `observations` | Configure RL observations | + | `events` | Configure RL events | + | `rewards` | Configure RL rewards | + | `terminations` | Configure RL terminations | + +:::{note} +Each embodiment should define its own action term as a low-level controller +that maps velocity commands to joint positions. We have pre-defined action +terms for different robot embodiments under +[`compass/rl_env/exts/mobility_es/mobility_es/mdp/action`](https://github.com/NVlabs/COMPASS/tree/main/compass/rl_env/exts/mobility_es/mobility_es/mdp/action) +that can be re-configured for new embodiments. +::: + +### 3. Register and verify the environment + +- Import your new environment in [`run.py`](https://github.com/NVlabs/COMPASS/blob/main/run.py). +- Verify it is functional by running `run.py` with the new environment. + +### Examples of existing embodiments + +- [H1 humanoid robot](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/h1_env_cfg.py) +- [Carter mobile robot](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/carter_env_cfg.py) +- [Spot quadruped robot](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/spot_env_cfg.py) +- [G1 humanoid robot](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/g1_env_cfg.py) +- [Digit humanoid robot](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/digit_env_cfg.py) + +## Adding new scenes + +### 1. Create scene configuration + +- Follow the Isaac Lab + [scene tutorial](https://isaac-sim.github.io/IsaacLab/main/source/tutorials/02_scene/create_scene.html) + to add a new scene configuration in + [`compass/rl_env/exts/mobility_es/mobility_es/config/environments.py`](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py). + Inherit from `EnvSceneAssetCfg` with proper parameters like + `pose_sample_range`, `env_spacing`, `replicate_physics`, etc. + +### 2. Add an occupancy map (optional) + +We use an occupancy map for collision-free pose sampling. It's optional but +recommended for improving sampling efficiency, especially in tight scenes. + +Generate the map directly from the scene USD with the bundled CLI: + +```bash +${ISAACLAB_PATH}/isaaclab.sh -p scripts/generate_omap_from_usd.py path/to/scene.usd +``` + +By default this writes `.png` and `occupancy_map.yaml` to +`/omap/`. Use `--cell-size`, `--z-min` / `--z-max`, `--bounds`, or +`--out-dir` to override the defaults; see `--help` for the full flag list. + +You can register the YAML path explicitly in `OMAP_PATHS` in +[`compass/rl_env/exts/mobility_es/mobility_es/config/environments.py`](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/config/environments.py), +but it's not required: when the scene has no entry, the collision checker +falls back to looking for an `omap/occupancy_map.yaml` next to the scene's +USD — which is exactly where the generator drops it. + +The legacy interactive flow via Isaac Sim's +[occupancy-map UI](https://docs.omniverse.nvidia.com/isaacsim/latest/features/ext_omni_isaac_occupancy_map.html) +still works if you prefer authoring a map by hand. Save the YAML/PNG into +the same `/omap/` location and the loader will pick it up. + +See the [Auto OMap from USDs](omap.md) handbook page for the deeper +generator + verifier reference. + +### 3. Register and verify the scene + +- Import your new scene in [`run.py`](https://github.com/NVlabs/COMPASS/blob/main/run.py). +- Verify the scene is functional by running `run.py` with the new scene + by setting `env_cfg.scene.environment = MyNewSceneCfg()`. diff --git a/docs/handbook/index.md b/docs/handbook/index.md new file mode 100644 index 0000000..763b014 --- /dev/null +++ b/docs/handbook/index.md @@ -0,0 +1,157 @@ +# COMPASS Handbook + +```{raw} html +
+COMPASS is a cross-embodiment mobility policy framework that +combines imitation learning, residual reinforcement learning, and policy +distillation to ship one generalist navigation policy across humanoids, +quadrupeds, and wheeled robots. This handbook is the live reference for +installing, training, deploying, and extending it. +
+
+ Residual RL + Policy Distillation + Isaac Lab 3.0 + ROS2 deployment + OSMO cloud + VLA fine-tuning +
+``` + +[**↗ Project page**](https://nvlabs.github.io/COMPASS/) · +[arXiv](https://arxiv.org/abs/2502.16372) · +[Source on GitHub](https://github.com/NVlabs/COMPASS) + +--- + +## End-to-end pipeline + +Each stage has a top-level entry-point script in the repo: + +| # | Stage | Script | +|---|------|--------| +| 1 | Train residual RL specialist (or evaluate any policy) | [`run.py`](https://github.com/NVlabs/COMPASS/blob/main/run.py) | +| 2 | Roll out specialists to collect HDF5 distillation data | [`record.py`](https://github.com/NVlabs/COMPASS/blob/main/record.py) | +| 3 | Distil specialists into one generalist | [`distillation_train.py`](https://github.com/NVlabs/COMPASS/blob/main/distillation_train.py) | +| 4 | Export to ONNX / JIT | [`onnx_conversion.py`](https://github.com/NVlabs/COMPASS/blob/main/onnx_conversion.py) | +| 5 | Convert ONNX → TensorRT engine | [`trt_conversion.py`](https://github.com/NVlabs/COMPASS/blob/main/trt_conversion.py) | +| 6 | Deploy via ROS2 in Isaac Sim or on real robots | [`ros2_deployment/`](https://github.com/NVlabs/COMPASS/tree/main/ros2_deployment) | + +Built on Isaac Lab 3.0 / Isaac Sim 4.5 and on top of NVIDIA's +[X-Mobility](https://github.com/NVlabs/X-MOBILITY) base policy. + +--- + +## Where to start + +::::{grid} 1 1 2 3 +:gutter: 3 + +:::{grid-item-card} 🚀 Quick start +:link: quickstart +:link-type: doc + +Three commands to a training shell. +::: + +:::{grid-item-card} 📦 Installation +:link: installation/docker +:link-type: doc + +Docker-as-venv: three commands and you're in a training shell. +::: + +:::{grid-item-card} 🎓 Training a specialist +:link: workflows/training +:link-type: doc + +How the residual RL loop works, how to override embodiment / scene. +::: + +:::{grid-item-card} 🛰️ ROS2 Deployment +:link: deployment/ros2 +:link-type: doc + +ROS2 nodes consuming a TensorRT engine — Isaac Sim, sim2real, or +object navigation. +::: + +:::{grid-item-card} ☁️ OSMO cloud submission +:link: osmo +:link-type: doc + +Submit training and evaluation runs to NVIDIA's OSMO cluster. +::: + +:::{grid-item-card} 🤖 GR00T post-training +:link: workflows/gr00t_finetuning +:link-type: doc + +Use COMPASS distillation datasets to fine-tune NVIDIA's +[Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T) VLA model. +::: + +:::: + +--- + +## What's where in the repo + +```text +COMPASS/ +├── compass/ +│ ├── residual_rl/ # Actor-critic that adds a residual on top of X-Mobility +│ ├── distillation/ # Generalist distillation (PyTorch Lightning trainer) +│ ├── rl_env/ # Isaac Lab `mobility_es` extension (embodiments + scenes) +│ └── utils/ # Shared helpers +├── configs/ # Gin configs (train / eval / record / distillation / shared) +├── docker/ # Docker-as-venv dev environment + Dockerfiles +├── osmo/ # OSMO workflow YAMLs + Python launcher +├── ros2_deployment/ # ROS2 packages for Isaac Sim / real-robot deployment +├── scripts/ # Standalone tools (HDF5→LeRobot, omap generator, …) +└── x_mobility/ # Vendored X-Mobility wheel +``` + +For deeper layout details, see [CLAUDE.md](https://github.com/NVlabs/COMPASS/blob/main/CLAUDE.md). + +```{toctree} +:caption: Installation +:maxdepth: 1 +:hidden: + +quickstart +installation/docker +agentic +extending +``` + +```{toctree} +:caption: Workflows +:maxdepth: 1 +:hidden: + +workflows/training +workflows/recording +workflows/distillation +workflows/export +workflows/gr00t_finetuning +``` + +```{toctree} +:caption: ROS2 Deployment +:maxdepth: 1 +:hidden: + +deployment/ros2 +deployment/isaac_sim +``` + +```{toctree} +:caption: Reference +:maxdepth: 1 +:hidden: + +osmo +omap +contributing +``` diff --git a/docs/handbook/installation/docker.md b/docs/handbook/installation/docker.md new file mode 100644 index 0000000..7690cbd --- /dev/null +++ b/docs/handbook/installation/docker.md @@ -0,0 +1,166 @@ +# Docker-as-venv install + +`./docker/run.sh` + `source ./docker/activate` give you a Python-venv-like dev +loop that runs commands inside the COMPASS container while keeping your editor, +terminal, and prompt on the host. Edit code with whatever editor you like; the +bind-mount means changes hot-reload. + +:::{note} +Docker is the recommended path because the image bakes Isaac Lab, the +X-Mobility wheel, and the `mobility_es` extension at build time — no host +Python / Vulkan SDK to manage. Bare-metal install is possible but not +documented end-to-end in the handbook; see the +[Isaac Lab installation guide](https://isaac-sim.github.io/IsaacLab/v3.0.0-beta1/source/setup/installation/index.html) +and `${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -e compass/rl_env/exts/mobility_es`. +::: + +## How it works + +``` +Host shell → shim PATH → docker exec → daemon container +(editor, (set by (translates (compass-rl image, + terminal, `source host CWD repo bind-mounted + prompt) docker/ to container at /workspace/COMPASS) + activate`) path) +``` + +`source ./docker/activate` brings up the container if it isn't already running, +prepends a tmp shim dir to `PATH`, and rewrites your prompt to `(compass-rl)`. +The shims (`python`, `pip`, `tensorboard`, `pytest`, `yapf`, `pylint`, +`pre-commit`, …) each `docker exec` the same-named binary inside the container, +with the host CWD translated to the container path. `deactivate` reverts. + +## Prerequisites + +- Docker (Engine 24+ recommended) +- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) so `--gpus all` works +- An NVIDIA GPU +- A HuggingFace account with access to [nvidia/COMPASS](https://huggingface.co/nvidia/COMPASS) and a [token](https://huggingface.co/settings/tokens) +- An X server (only if you want `--viz kit` to render — headless training works fine without) + +For the three-command quick-start path, see [Quick start](../quickstart.md). +The rest of this page is the subcommand reference, multi-checkout / multi-GPU +notes, the git workflow, and troubleshooting. + +## Subcommand reference + +``` +./docker/run.sh build [tag] # docker build -f docker/Dockerfile.rl +./docker/run.sh assets [--hf-token TOK] # download USDs + ckpt to ./assets +./docker/run.sh up # idempotent: start daemon container +./docker/run.sh down # docker rm -f the container +./docker/run.sh exec [...] # one-shot exec inside the container +./docker/run.sh shell # escape hatch: drop into bash +./docker/run.sh status # show container + image state +``` + +### `build` + +Builds `compass-rl:latest` (override the tag with `./docker/run.sh build mytag`). +Re-running is fast because Docker layer-caches. + +### `assets` + +Downloads: + +| File | URL | Lands at | +|------|-----|---------| +| `compass_usds.zip` (extracted) | `huggingface.co/nvidia/COMPASS/...` | `./assets/usd/` | +| `x_mobility-nav2-semantic_action_path.ckpt` | `huggingface.co/nvidia/X-Mobility/...` | `./assets/x_mobility.ckpt` | + +Cache-aware: skips files that already exist. `--force` to redownload everything. +`--cache-dir DIR` to put assets somewhere other than `./assets/`. + +`./assets/` is part of the repo bind-mount, so inside the container it's at +`/workspace/COMPASS/assets/` — relative paths from the repo root work in both +host and container shells. + +### `up` / `down` + +`up` is idempotent: starts the container if not running, or no-op if already up. +`down` stops and removes it. The container's name is +`compass--<8-hex-of-repo-path>`, so multiple checkouts of COMPASS on +the same machine coexist without colliding. + +### `exec` / `shell` + +`exec` runs one command inside the container (`./docker/run.sh exec python --version`). +`shell` drops you into a bash inside the container — escape hatch for poking at +the image, not for daily dev (use `source ./docker/activate` for that). + +### `status` + +Shows whether the image is built, whether the container is running, and the +relevant paths. Run this first when something feels off. + +## Multiple checkouts of COMPASS + +The container name hashes the absolute repo path: + +```bash +# ~/COMPASS/dev → container compass-liuw-a7f3b2c8 +# ~/COMPASS/main → container compass-liuw-9d1e44f0 +``` + +Both can run concurrently and never confuse each other. Each gets its own +bind-mount, its own pre-commit cache, its own kit shader cache. + +## Multi-GPU + +The container starts with `--gpus all` by default. To pin to specific GPUs, +export `NVIDIA_VISIBLE_DEVICES` before bringing the container up: + +```bash +NVIDIA_VISIBLE_DEVICES=0 ./docker/run.sh up # only GPU 0 visible inside the container +``` + +(Note: this currently requires editing `_compass_run_args` in +[`docker/run.sh`](https://github.com/NVlabs/COMPASS/blob/main/docker/run.sh) +to honor the env var — track via `release_tracker.md` if you need it; not +implemented in v1.) + +## VSCode / Cursor + +Editor + container are decoupled: VSCode runs natively on the host, opens the +repo at `~/Projects/COMPASS`, and you run `python run.py …` from VSCode's +integrated terminal **after** sourcing `./docker/activate` in that terminal. No +"Reopen in Container" needed. + +## Git workflow + +`git status / add / commit -s / push` run on the host as normal — no shim. SSH +keys, GPG signing, DCO sign-off all work without extra plumbing. + +The only wrinkle is **pre-commit hooks** (yapf, pylint, clang-format). The +hooks live in the container, not on the host. So: + +- **Activate before committing.** `source ./docker/activate` adds `pre-commit` + to `PATH`, and the shim routes the hook invocation into the container. +- **Hook env cache** lives at `./.cache/pre-commit/` (inside the bind-mount), so + `down` + `up` doesn't force a 30s rebuild. `.cache/` is gitignored. + +## Troubleshooting + +| Symptom | Likely cause | Fix | +|--------|--------------|-----| +| `Image compass-rl:latest not found` | Haven't built yet | `./docker/run.sh build` | +| `permission denied while trying to connect to the Docker daemon` | User not in `docker` group | `sudo usermod -aG docker $USER` then re-login | +| `Vulkan/X11: cannot open display` from inside container | X server not forwarded | `xhost +SI:localuser:$(id -un)` (run.sh tries this automatically; may need manual on systems where it fails silently) | +| Isaac Sim shader cold-compile every run | Shader cache wiped | Check `~/.cache/compass/kit/` is writable; reuses across runs | +| `pre-commit: command not found` on `git commit` | Forgot to activate | `source ./docker/activate` | +| Container won't `up`: "another container with the same name" | Stale container from a crash | `./docker/run.sh down` then `./docker/run.sh up` | + +## What lives where + +``` +docker/ +├── Dockerfile.rl # Image: Isaac Lab 3.0-beta1 + COMPASS deps + python wrapper +├── Dockerfile.distillation # Used by docker-only distillation runs (unrelated to dev env) +├── run.sh # build / assets / up / down / exec / shell / status +├── activate # source me: shim PATH + (compass-rl) prompt prefix +└── prepare_assets.sh # invoked by `run.sh assets` + +~/.cache/compass/kit/ # writable Isaac Sim shader cache (survives container teardown) +./assets/ # USDs + X-Mobility ckpt (gitignored; populated by `run.sh assets`) +./.cache/ # pip / pre-commit / huggingface caches (gitignored) +``` diff --git a/docs/handbook/omap.md b/docs/handbook/omap.md new file mode 100644 index 0000000..8c49686 --- /dev/null +++ b/docs/handbook/omap.md @@ -0,0 +1,64 @@ +# Auto OMap from USDs + +Occupancy maps gate collision-free pose sampling during training. Historically +they were hand-authored in the Isaac Sim editor — slow, error-prone, and a +blocker for [SAGE-driven](agentic.md) scene generation. The +[`scripts/generate_omap_from_usd.py`](https://github.com/NVlabs/COMPASS/blob/main/scripts/generate_omap_from_usd.py) +CLI generates an OMap directly from a USD scene in one shot. + +## Generate an OMap + +```bash +python scripts/generate_omap_from_usd.py \ + compass/rl_env/exts/mobility_es/mobility_es/usd//.usd +``` + +(Inside an [activated dev shell](installation/docker.md), `python` already +points at Isaac Sim's bundled Python; on bare-metal install, prefix with +`${ISAACLAB_PATH}/isaaclab.sh -p`.) + +By default the script writes the PNG + ROS YAML pair to `/omap/` +next to the input USD — exactly where +[`OccupancyMapCollisionChecker`](https://github.com/NVlabs/COMPASS/blob/main/compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py) +auto-discovers it when no explicit `OMAP_PATHS` entry exists. + +## Common flags + +| Flag | Default | Meaning | +|------|--------|---------| +| `-o, --out-dir` | `/omap/` | Where the PNG + YAML land | +| `--cell-size` | `0.05` | meters / pixel | +| `--z-min` / `--z-max` | `0.1` / `0.62` | Height slab the rasterizer projects | +| `--bounds` | derived from USD bbox + 1 m padding | World-space `xmin xmax ymin ymax` | +| `--padding` | `1.0` | Padding (m) around the auto-derived bbox | +| `--map-name` | `` | PNG stem | + +Run `python scripts/generate_omap_from_usd.py --help` for the complete list. + +## Loader auto-discovery + +`OccupancyMapCollisionChecker` (`compass/rl_env/exts/.../utils/occupancy_map.py`) +checks `OMAP_PATHS` first; if it has no entry for the scene, it falls back to +`/omap/occupancy_map.yaml` — the same path the +generator writes to. **No `OMAP_PATHS` registration is needed for new +scenes.** + +## Verifying a generated OMap + +A small standalone verifier in `/tmp/verify_omap.py` (used during PR-5 +development) loads the generated YAML, samples a uniform grid of poses, +runs `is_in_collision`, and writes annotated PNGs: + +- All 300 samples — green = free, red = collision (or out-of-bounds). +- Free-only — every dot lands on an unoccupied (white) cell. + +The verification checked three representative scenes (`office`, +`combined_simple_warehouse`, `sample_small_footprint_one_rack_obst_sdg`); +all passed with free-pose samples cleanly avoiding obstacles. + +## Slab tuning for taller embodiments + +The default `[0.1, 0.62]` height slab works for Carter, Spot, G1. Tall +humanoids (H1) clear about 1.3 m; bump `--z-max 1.4` so the rasterizer +captures higher obstacles. The slab is the only embodiment-specific knob +and is exposed at the CLI rather than baked into per-embodiment maps. diff --git a/docs/handbook/osmo.md b/docs/handbook/osmo.md new file mode 100644 index 0000000..6eca7b9 --- /dev/null +++ b/docs/handbook/osmo.md @@ -0,0 +1,128 @@ +# OSMO cloud submission + +Submit COMPASS training, evaluation, recording, or distillation jobs to +NVIDIA's [OSMO](https://docs.nvidia.com/osmo/) cluster via the bundled +Python launcher [`osmo/run_osmo.py`](https://github.com/NVlabs/COMPASS/blob/main/osmo/run_osmo.py). + +## Prerequisites + +- An OSMO account with a logged-in CLI: `osmo login` +- Write access to a docker registry that OSMO workers can pull from (set as `--registry-prefix` or via the `COMPASS_OSMO_REGISTRY` env var, e.g. `nvcr.io//`) +- A wandb account; export `WANDB_API_KEY` (or pass `--prompt`) +- A HuggingFace token with read access to [`nvidia/COMPASS`](https://huggingface.co/nvidia/COMPASS) (gated) and [`nvidia/X-Mobility`](https://huggingface.co/nvidia/X-Mobility); export `HF_TOKEN` (or pass `--prompt`). Distillation does not need this. +- Any resume / residual / distillation checkpoints you reference uploaded as wandb artifacts that the workflow can `wandb artifact get`. + +The COMPASS USDs (`compass_usds.zip`) and the X-Mobility base-policy +checkpoint are downloaded inside the workflow directly from HuggingFace +on every run — no OSMO-dataset or wandb-artifact mirroring required. + +## Quick start + +```bash +# One-time, in your shell +export WANDB_API_KEY= +export HF_TOKEN= +export COMPASS_OSMO_REGISTRY=nvcr.io// + +# Submit residual RL training; build+push the image automatically. +python osmo/run_osmo.py train \ + --experiment-name pilot \ + --wandb-project compass-rl +``` + +To inspect the would-be `osmo workflow submit` invocation without actually +submitting, add `--dry-run`. + +## Subcommands + +| Subcommand | Workflow YAML | Purpose | +|------------|---------------|---------| +| `train` | [`osmo/workflows/rl_es_train_workflow.yaml`](https://github.com/NVlabs/COMPASS/blob/main/osmo/workflows/rl_es_train_workflow.yaml) | Residual RL specialist training (auto-runs an eval at the end) | +| `eval` | [`osmo/workflows/rl_es_eval_workflow.yaml`](https://github.com/NVlabs/COMPASS/blob/main/osmo/workflows/rl_es_eval_workflow.yaml) | Residual RL specialist or generalist evaluation | +| `record` | [`osmo/workflows/rl_es_record_workflow.yaml`](https://github.com/NVlabs/COMPASS/blob/main/osmo/workflows/rl_es_record_workflow.yaml) | Roll out a specialist policy to collect HDF5 data for distillation | +| `distill` | [`osmo/workflows/distillation_train_workflow.yaml`](https://github.com/NVlabs/COMPASS/blob/main/osmo/workflows/distillation_train_workflow.yaml) | Train the generalist distillation policy | + +A separate launcher [`osmo/run_benchmark.py`](https://github.com/NVlabs/COMPASS/blob/main/osmo/run_benchmark.py) reuses the eval workflow above to fire a no-regression benchmark sweep across scenes for a given embodiment — see [Benchmark](#benchmark) below. + +### `train` + +```bash +python osmo/run_osmo.py train \ + --experiment-name \ + --wandb-project \ + [--resume-ckpt ] \ + [--no-residual] \ + [--image ] +``` + +### `eval` + +```bash +python osmo/run_osmo.py eval \ + --experiment-name \ + --wandb-project \ + --checkpoint \ + [--distillation-ckpt ] \ + [--no-residual] \ + [--embodiment h1|spot|carter|g1|digit] \ + [--environment ] \ + [--image ] +``` + +### `record` + +```bash +python osmo/run_osmo.py record \ + --experiment-name \ + --dataset-name \ + [--image ] +``` + +### `distill` + +```bash +python osmo/run_osmo.py distill \ + --experiment-name \ + --wandb-project \ + --dataset-name \ + [--checkpoint ] \ + [--train-config distillation_config] \ + [--image ] +``` + +## Benchmark + +Use [`osmo/run_benchmark.py`](https://github.com/NVlabs/COMPASS/blob/main/osmo/run_benchmark.py) to sweep a checkpoint across multiple scenes for one embodiment in a single invocation. Each `--environments` entry fires one `rl_es_eval_workflow.yaml` submission; results land in W&B under `bm___` with the usual `eval/goal_reached_rate`, `eval/fall_down_rate`, `eval/total_travel_time`, and `eval/weighted_travel_time` metrics. Re-run with different `--embodiment` values for full matrix coverage. + +```bash +python osmo/run_benchmark.py \ + --experiment-name \ + --wandb-project-name \ + --checkpoint-artifact \ + [--distillation-ckpt-artifact ] \ + [--embodiment h1|spot|carter|g1|digit] \ + [--environments simple_office warehouse_single_rack ...] \ + [--image-name ] +``` + +Default sweep covers `simple_office`, `warehouse_single_rack`, `warehouse_multi_rack`, `combined_single_rack`, `combined_multi_rack` with `--embodiment h1`. Prerequisites and `--registry-prefix` / `--image-name` / `--dry-run` / `--prompt` behavior are the same as `run_osmo.py`. + +## Image build behavior + +If you do not pass `--image`, `run_osmo.py` will: + +1. Build the appropriate Dockerfile ([`docker/Dockerfile.rl`](https://github.com/NVlabs/COMPASS/blob/main/docker/Dockerfile.rl) for `train` / `eval` / `record`, [`docker/Dockerfile.distillation`](https://github.com/NVlabs/COMPASS/blob/main/docker/Dockerfile.distillation) for `distill`) tagged as `/compass_:`. +2. `docker push` that tag. +3. Pass the resulting image into the `osmo workflow submit` command. + +For repeated runs against the same code, build the image once and pass it via +`--image` to skip the build/push. + +## Troubleshooting + +- **`ERROR: $WANDB_API_KEY is not set`** — export it, or pass `--prompt` to be asked interactively. +- **`ERROR: --image not given and --registry-prefix is empty`** — either supply `--image ` or set `--registry-prefix` / `$COMPASS_OSMO_REGISTRY`. +- **`manifest unknown` from `osmo workflow submit`** — the image hasn't pushed yet (or you don't have read access). Re-run `docker push ` and retry. +- **Workflow logs** — visit your OSMO console; the workflow ID printed by `osmo workflow submit` is the lookup key. +- **HuggingFace download fails inside the workflow** — verify `HF_TOKEN` was exported and that your account has access to [`nvidia/COMPASS`](https://huggingface.co/nvidia/COMPASS) and [`nvidia/X-Mobility`](https://huggingface.co/nvidia/X-Mobility). +- **Dataset not found** (distillation only) — confirm your distillation input dataset is uploaded and visible to the workflow's run-as account. diff --git a/docs/handbook/quickstart.md b/docs/handbook/quickstart.md new file mode 100644 index 0000000..f117de3 --- /dev/null +++ b/docs/handbook/quickstart.md @@ -0,0 +1,78 @@ +# Quick start + +The fastest path to a training shell, using the [Docker-as-venv dev environment](installation/docker.md). + +## Prerequisites + +- Docker (Engine 24+) with the + [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) +- An NVIDIA GPU +- A HuggingFace account with [token](https://huggingface.co/settings/tokens) + and access to [nvidia/COMPASS](https://huggingface.co/nvidia/COMPASS) + +## Three commands + +```bash +git clone https://github.com/NVlabs/COMPASS.git && cd COMPASS + +export HF_TOKEN=hf_xxx +./docker/run.sh assets # USDs + X-Mobility ckpt → ./assets/ (~5 min, one-time) +./docker/run.sh build # build the dev image (~10 min, first run) +source ./docker/activate # venv-like activation (prompt: (compass-rl)) +``` + +:::{admonition} What `./docker/run.sh assets` downloads +:class: tip + +The build step doesn't pull these — training does. The downloader is gated +by your HF token. + +- **`./assets/usd/`** — `compass_usds.zip` from + [`nvidia/COMPASS`](https://huggingface.co/nvidia/COMPASS). The training + scenes (warehouse, office, hospital, …) and embodiment USDs. +- **`./assets/x_mobility.ckpt`** — `x_mobility-nav2-semantic_action_path.ckpt` + from [`nvidia/X-Mobility`](https://huggingface.co/nvidia/X-Mobility). The + base policy that the residual head is trained on top of. + +`./assets/` is part of the repo bind-mount, so inside the container these +paths are visible at the same `./assets/...` (under `/workspace/COMPASS/`). +Re-running `assets` is idempotent — it's a no-op once the files exist. +::: + +## First training step + +From the activated shell, kick off a 1-env smoke run to confirm everything wires: + +```bash +python run.py \ + -c configs/train_config.gin \ + -o /tmp/out \ + -b ./assets/x_mobility.ckpt \ + --num_envs 1 \ + --enable_cameras +``` + +You should see Isaac Sim boot, the scene load, and PPO step into iteration 0. + +## Next steps + +- **Scale up**: drop `--num_envs 1`. The default scales with available VRAM (~1 GB / env). +- **Other embodiments**: pass `--embodiment {h1,carter,spot,g1,digit}`. See + [Adding a new embodiment / scene](extending.md) to add your own. +- **Submit to OSMO**: see [OSMO cloud submission](osmo.md). +- **Distil a generalist**: collect rollouts ([recording](workflows/recording.md)) + then [distil](workflows/distillation.md). + +## Leaving the dev environment + +```bash +deactivate # remove the shim PATH; keep the container alive +./docker/run.sh down # stop the container entirely +``` + +If you can't run Docker, you'll need to install Isaac Lab v3.0.0-beta1 and +the `mobility_es` extension on the host directly — follow the +[Isaac Lab installation guide](https://isaac-sim.github.io/IsaacLab/v3.0.0-beta1/source/setup/installation/index.html) +and then `${ISAACLAB_PATH}/isaaclab.sh -p -m pip install -e compass/rl_env/exts/mobility_es`. +Docker is the supported / tested path; bare-metal works but isn't documented +end-to-end in the handbook. diff --git a/docs/handbook/requirements.txt b/docs/handbook/requirements.txt new file mode 100644 index 0000000..e4ecce4 --- /dev/null +++ b/docs/handbook/requirements.txt @@ -0,0 +1,8 @@ +myst-parser>=3.0.1 +nvidia-sphinx-theme>=0.0.8 +setuptools>=69 +sphinx>=7.0,<8.3 +sphinx-autobuild>=2024.0.0 +sphinx_copybutton>=0.5.2 +sphinx_design>=0.6.1 +sphinxcontrib-mermaid>=1.0.0 diff --git a/docs/handbook/workflows/distillation.md b/docs/handbook/workflows/distillation.md new file mode 100644 index 0000000..0194efc --- /dev/null +++ b/docs/handbook/workflows/distillation.md @@ -0,0 +1,54 @@ +# Distilling the generalist + +Once you have an HDF5 distillation dataset (see [Recording distillation data](recording.md)), +`distillation_train.py` trains a single generalist policy that imitates the +per-embodiment specialists. PyTorch Lightning under the hood; pure-PyTorch +script (no Isaac-Lab Python wrapper required). + +## Train a generalist + +```bash +python3 distillation_train.py \ + --config-files configs/distillation_config.gin \ + --dataset-path \ + --output-dir +``` + +Hyperparameters live in +[`configs/distillation_config.gin`](https://github.com/NVlabs/COMPASS/blob/main/configs/distillation_config.gin). +Override on the CLI by repeating `--config-files` or via gin +`--gin_bindings` overrides if you need to tweak a single value. + +## Evaluate the generalist + +The same `run.py` driver evaluates a generalist checkpoint when given via `-d`: + +```bash +python run.py \ + -c configs/eval_config.gin \ + -o \ + -b \ + -d \ + --enable_cameras \ + --video \ + --video_interval +``` + +(Inside an [activated dev shell](../installation/docker.md), `python` is +already wired; on bare-metal install, prefix with `${ISAACLAB_PATH}/isaaclab.sh -p`.) + +## Submitting to OSMO + +```bash +python osmo/run_osmo.py distill \ + --experiment-name \ + --dataset-name +``` + +Full reference: [OSMO cloud submission](../osmo.md). + +## Where to go next + +- [Export](export.md) the generalist to ONNX / TensorRT for inference. +- [GR00T post-training](gr00t_finetuning.md) — use the same dataset to + fine-tune the Isaac-GR00T VLA model. diff --git a/docs/handbook/workflows/export.md b/docs/handbook/workflows/export.md new file mode 100644 index 0000000..2f47b93 --- /dev/null +++ b/docs/handbook/workflows/export.md @@ -0,0 +1,50 @@ +# Exporting to ONNX / TensorRT + +Two scripts cover the export path. Both are pure-PyTorch (no Isaac Lab +required). + +## ONNX / JIT + +### Specialist (residual + base) + +```bash +python3 onnx_conversion.py \ + -b \ + -r \ + -o \ + -j +``` + +### Generalist + +```bash +python3 onnx_conversion.py \ + -b \ + -g \ + -e \ + -o \ + -j +``` + +`-e` selects which embodiment-specific head to export. Pass one of +`{h1, carter, spot, g1, digit}` — must match an entry in `EmbodimentEnvCfgMap` +in [`run.py`](https://github.com/NVlabs/COMPASS/blob/main/run.py). + +## TensorRT + +```bash +python3 trt_conversion.py \ + -o \ + -t +``` + +Generates a TensorRT engine specialised for your GPU. The engine is then +consumed by the [ROS2 deployment node](../deployment/ros2.md). + +:::{note} Engine portability +TensorRT engines are GPU-specific. An engine built on an L40 won't run +optimally (or at all) on a Jetson Orin. Build the engine on the same GPU +family as the deployment target, or use the +[ROS2 deployment](../deployment/ros2.md) container's runtime conversion +flow. +::: diff --git a/docs/handbook/workflows/gr00t_finetuning.md b/docs/handbook/workflows/gr00t_finetuning.md new file mode 100644 index 0000000..def16fa --- /dev/null +++ b/docs/handbook/workflows/gr00t_finetuning.md @@ -0,0 +1,50 @@ +# GR00T post-training (VLA fine-tuning) + +COMPASS distillation datasets can fine-tune VLA models like +[NVIDIA Isaac-GR00T](https://github.com/NVIDIA/Isaac-GR00T) to bolt on +navigation capabilities. Three steps: convert, fine-tune, evaluate. + +## Step 1 — Convert HDF5 → GR00T LeRobot format + +You need an HDF5 distillation dataset first; see +[Recording distillation data](recording.md). Then convert with the bundled +script: + +```bash +python scripts/hdf5_to_lerobot_episodic.py \ + --hdf5-dir \ + --output-path +``` + +The script is pure-Python (no Isaac Lab) — runs anywhere with the standard +COMPASS Python environment. It walks the HDF5 dataset, repacks per-episode +into LeRobot's parquet format, and emits the chunk + metadata layout that +GR00T's training pipeline expects. + +## Step 2 — Post-train GR00T + +Follow the post-training instructions in the +[Isaac-GR00T getting-started guide](https://github.com/NVIDIA/Isaac-GR00T/tree/main/getting_started). + +A ready-to-use navigation data configuration lives on this branch: +[`liuw/nav_fine_tune`](https://github.com/NVIDIA/Isaac-GR00T/compare/main...liuw/nav_fine_tune). + +## Step 3 — Evaluate the post-trained GR00T model in COMPASS + +Launch the GR00T inference server (see the Isaac-GR00T repo) on +**port 8888**, with the same data configuration you used during training. +Then evaluate from COMPASS: + +```bash +python run.py \ + -c configs/eval_config.gin \ + -o \ + -b \ + --enable_cameras \ + --gr00t-policy +``` + +`--gr00t-policy` tells `run.py` to dispatch action queries to the inference +server instead of loading a local checkpoint. Eval parameters (scene, +embodiment, episode count) live in +[`configs/eval_config.gin`](https://github.com/NVlabs/COMPASS/blob/main/configs/eval_config.gin). diff --git a/docs/handbook/workflows/recording.md b/docs/handbook/workflows/recording.md new file mode 100644 index 0000000..1023ce5 --- /dev/null +++ b/docs/handbook/workflows/recording.md @@ -0,0 +1,45 @@ +# Recording distillation data + +`record.py` rolls out trained specialists across embodiments / scenes and +writes the trajectories as HDF5 files. The resulting dataset feeds the +[generalist distillation step](distillation.md). + +## Run a recording + +1. **Configure which specialists to record from**: edit + [`configs/distillation_dataset_config_template.yaml`](https://github.com/NVlabs/COMPASS/blob/main/configs/distillation_dataset_config_template.yaml) + to point at your per-embodiment specialist checkpoints and split ratios. + Save it as `configs/distillation_dataset_config.yaml` (or anywhere; pass + the path on the command line). + +2. **Roll out**: + + ```bash + python record.py \ + -c configs/distillation_dataset_config.yaml \ + -o \ + -b \ + --dataset-name + ``` + +The dataset name appears in OSMO references and in the HDF5 file metadata. It +is the same identifier used by [`scripts/hdf5_to_lerobot_episodic.py`](https://github.com/NVlabs/COMPASS/blob/main/scripts/hdf5_to_lerobot_episodic.py) +for [GR00T post-training](gr00t_finetuning.md). + +## Output layout + +`record.py` writes one HDF5 file per shard, plus a manifest, under +`/`. Each HDF5 file contains episodic data with the standard +COMPASS observation keys (camera, lidar, proprio, …) and the action emitted +by the specialist. The shape convention is `[T, S, ...]` (timesteps × shards). + +## On OSMO + +```bash +python osmo/run_osmo.py record \ + --experiment-name \ + --dataset-name \ + --base-policy-ckpt +``` + +Full reference: [OSMO cloud submission](../osmo.md). diff --git a/docs/handbook/workflows/training.md b/docs/handbook/workflows/training.md new file mode 100644 index 0000000..ee74b66 --- /dev/null +++ b/docs/handbook/workflows/training.md @@ -0,0 +1,78 @@ +# Training residual RL specialists + +`run.py` trains a residual RL policy on top of the X-Mobility base policy for a +given embodiment + scene. The residual head adapts X-Mobility's +language-conditioned navigation behaviour to embodiment-specific dynamics. + +## Default training run + +```bash +python run.py \ + -c configs/train_config.gin \ + -o \ + -b \ + --enable_cameras +``` + +(Inside an [activated dev shell](../installation/docker.md), `python` already +points at Isaac Sim's bundled Python; on a bare-metal install, prefix with +`${ISAACLAB_PATH}/isaaclab.sh -p`.) + +## Evaluating a trained specialist + +```bash +python run.py \ + -c configs/eval_config.gin \ + -o \ + -b \ + -p \ + --enable_cameras \ + --video \ + --video_interval +``` + +## GPU memory and `--num_envs` + +GPU memory scales linearly with the parallel-env count: **32 envs ≈ 30 GB**. +Drop `--num_envs` to fit the host. `--num_envs 1` is the canonical smoke-test +setting and reaches PPO iteration 0 in a few minutes. + +## Picking embodiment / scene + +Override the gin defaults via CLI: + +```bash +python run.py -c configs/train_config.gin \ + --embodiment {h1,carter,spot,g1,digit} \ + --environment \ + -o -b +``` + +The current set lives in `EmbodimentEnvCfgMap` and `EnvSceneAssetCfgMap` in +[`run.py`](https://github.com/NVlabs/COMPASS/blob/main/run.py). To register a +new one, see [Adding a new embodiment / scene](../extending.md). + +## Logging + +TensorBoard at `/tensorboard/` by default. For Weights & Biases +add: + +```bash +--logger wandb \ +--wandb-project-name \ +--wandb-run-name \ +--wandb-entity-name +``` + +## Submitting to OSMO + +Train on the OSMO cluster instead of locally: + +```bash +python osmo/run_osmo.py train \ + --experiment-name \ + --image \ + --base-policy-ckpt +``` + +Full reference: [OSMO cloud submission](../osmo.md). diff --git a/docs/project_page/LICENSE b/docs/project_page/LICENSE new file mode 100644 index 0000000..261eeb9 --- /dev/null +++ b/docs/project_page/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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+ COMPASS: Cross-embOdiment Mobility Policy
+ via ResiduAl RL and Skill Synthesis +

+
+ + Wei Liu1, + + Huihua Zhao1, + + Chenran Li1,2, + + + Yuchen Deng1, + + + Joydeep Biswas1,3, + + + Soha Pouya1, + + + Yan Chang1 + +
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+ 1NVIDIA, + 2UC Berkeley + 3UT Austin +
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+ + + + + + Paper + + + + + + + + arXiv + + + + + + + + + Code + + + + + + + + + Docs + + +
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Abstract

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+ As robots are increasingly deployed in diverse application domains, enabling robust mobility across different embodiments has become a critical challenge. Classical mobility stacks, though effective on specific platforms, require extensive per-robot tuning and do not scale easily to new embodiments. Learning-based approaches, such as imitation learning (IL), offer alternatives, but face significant limitations on the need for high-quality demonstrations for each embodiment. +

+

+ To address these challenges, we introduce COMPASS, a unified framework that enables scalable cross-embodiment mobility using expert demonstrations from only a single embodiment. We first pre-train a mobility policy on a single robot using IL, combining a world model with a policy model. We then apply residual reinforcement learning (RL) to efficiently adapt this policy to diverse embodiments through corrective refinements. Finally, we distill specialist policies into a single generalist policy conditioned on an embodiment embedding vector. This design significantly reduces the burden of collecting data while enabling robust generalization across a wide range of robot designs. Our experiments demonstrate that COMPASS scales effectively across diverse robot platforms while maintaining adaptability to various environment configurations, achieving a generalist policy with a success rate approximately 5X higher than the pre-trained IL policy, and further demonstrates zero-shot sim-to-real transfer. +

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Method

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+ Method overview +
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+ COMPASS introduces a novel three-stage learning workflow for developing cross-embodiment mobility policies: +

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  1. Imitation Learning: We first train a foundational model that combines a world model with a mobility policy using easily accessible teacher policies.
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  2. Residual RL: Building on the base policy, we employ residual reinforcement learning to fine-tune embodiment-specific policies, handling various physical constraints and sensor modalities.
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  3. Policy Distillation: Finally, we merge these embodiment-specialist policies into a single robust cross-embodiment policy through policy distillation.
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+ This approach enables efficient transfer of mobility skills across diverse robot platforms while maintaining adaptability to various environment configurations. +

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Results

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+ Extensive experiments demonstrate that COMPASS can achieve robust generalization across diverse robot platforms while preserving the adaptability needed to succeed in varied environments. Quantitatively, the RL specialists and the distilled generalist policy can achieve a 5X higher success rate and 3X lower travel time on average than the pre-trained IL policy (X-Mobility). +

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+ Benchmark Results +
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Zero-shot Sim2Real Transfer

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+ COMPASS policies trained in simulation can be directly deployed on real robots, demonstrating strong sim2real transfer capabilities without additional fine-tuning. +

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Real-world deployment of COMPASS policy on real robots: Carter and G1.

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Open Vocabulary Object Navigation

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+ Open Vocabulary Object Navigation by integrating Locate3D with COMPASS. +

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COMPASS policy performing open vocabulary object navigation by integrating Locate3D.

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GR00T Post-training with COMPASS Datasets

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+ GR00T Post-training with COMPASS distillation datasets, enabling navigation capabilities. +

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GR00T post-training with COMPASS datasets for navigation.

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BibTeX

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+    @article{liu2025compass,
+      title={COMPASS: Cross-embodiment Mobility Policy via Residual RL and Skill Synthesis},
+      author={Liu, Wei and Zhao, Huihua and Li, Chenran and Deng, Yuchen and Biswas, Joydeep and Pouya, Soha and Chang, Yan},
+      journal={arXiv preprint arXiv:2502.16372},
+      year={2025}
+    }
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+ Page template borrowed from Nerfies. +

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border-radius: 10px !important; +} + +.publication-video iframe { + position: absolute; + top: 0; + left: 0; + width: 100%; + height: 100%; +} + +.publication-body img { +} + +.results-carousel { + overflow: hidden; +} + +.results-carousel .item { + margin: 5px; + overflow: hidden; + border: 1px solid #bbb; + border-radius: 10px; + padding: 0; + font-size: 0; +} + +.results-carousel video { + margin: 0; +} diff --git a/docs/project_page/static/images/benchmark.png b/docs/project_page/static/images/benchmark.png new file mode 100644 index 0000000..2cdca96 --- /dev/null +++ b/docs/project_page/static/images/benchmark.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ad32d1440d219afa9a65bb2ce15d2c26aa845fc7f1c11ce6b6cfb9ec7466ec8e +size 182434 diff --git a/docs/project_page/static/images/method.png b/docs/project_page/static/images/method.png new file mode 100644 index 0000000..60391a0 --- /dev/null +++ b/docs/project_page/static/images/method.png @@ -0,0 +1,3 @@ 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__webpack_require__(moduleId) { +/******/ +/******/ // Check if module is in cache +/******/ if(installedModules[moduleId]) { +/******/ return installedModules[moduleId].exports; +/******/ } +/******/ // Create a new module (and put it into the cache) +/******/ var module = installedModules[moduleId] = { +/******/ i: moduleId, +/******/ l: false, +/******/ exports: {} +/******/ }; +/******/ +/******/ // Execute the module function +/******/ modules[moduleId].call(module.exports, module, module.exports, __webpack_require__); +/******/ +/******/ // Flag the module as loaded +/******/ module.l = true; +/******/ +/******/ // Return the exports of the module +/******/ return module.exports; +/******/ } +/******/ +/******/ +/******/ // expose the modules object (__webpack_modules__) +/******/ __webpack_require__.m = modules; +/******/ +/******/ // expose the module cache +/******/ __webpack_require__.c = installedModules; +/******/ +/******/ // define getter function for harmony exports +/******/ __webpack_require__.d = function(exports, name, getter) { +/******/ if(!__webpack_require__.o(exports, name)) { +/******/ Object.defineProperty(exports, name, { +/******/ configurable: false, +/******/ enumerable: true, +/******/ get: getter +/******/ }); +/******/ } +/******/ }; +/******/ +/******/ // getDefaultExport function for compatibility with non-harmony modules +/******/ __webpack_require__.n = function(module) { +/******/ var getter = module && module.__esModule ? +/******/ function getDefault() { return module['default']; } : +/******/ function getModuleExports() { return module; }; +/******/ __webpack_require__.d(getter, 'a', getter); +/******/ return getter; +/******/ }; +/******/ +/******/ // Object.prototype.hasOwnProperty.call +/******/ __webpack_require__.o = function(object, property) { return Object.prototype.hasOwnProperty.call(object, property); }; +/******/ +/******/ // __webpack_public_path__ +/******/ __webpack_require__.p = ""; +/******/ +/******/ // Load entry module and return exports +/******/ return __webpack_require__(__webpack_require__.s = 5); +/******/ }) +/************************************************************************/ +/******/ ([ +/* 0 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export addClasses */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "d", function() { return removeClasses; }); +/* unused harmony export show */ +/* unused harmony export hide */ +/* unused harmony export offset */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "e", function() { return width; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return height; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return outerHeight; }); +/* unused harmony export outerWidth */ +/* unused harmony export position */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return css; }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__type__ = __webpack_require__(2); + + +var addClasses = function addClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.add(cls); + }); +}; + +var removeClasses = function removeClasses(element, classes) { + classes = Array.isArray(classes) ? classes : classes.split(' '); + classes.forEach(function (cls) { + element.classList.remove(cls); + }); +}; + +var show = function show(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = ''; + }); +}; + +var hide = function hide(elements) { + elements = Array.isArray(elements) ? elements : [elements]; + elements.forEach(function (element) { + element.style.display = 'none'; + }); +}; + +var offset = function offset(element) { + var rect = element.getBoundingClientRect(); + return { + top: rect.top + document.body.scrollTop, + left: rect.left + document.body.scrollLeft + }; +}; + +// returns an element's width +var width = function width(element) { + return element.getBoundingClientRect().width || element.offsetWidth; +}; +// returns an element's height +var height = function height(element) { + return element.getBoundingClientRect().height || element.offsetHeight; +}; + +var outerHeight = function outerHeight(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var height = element.offsetHeight; + if (withMargin) { + var style = window.getComputedStyle(element); + height += parseInt(style.marginTop) + parseInt(style.marginBottom); + } + return height; +}; + +var outerWidth = function outerWidth(element) { + var withMargin = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + var width = element.offsetWidth; + if (withMargin) { + var style = window.getComputedStyle(element); + width += parseInt(style.marginLeft) + parseInt(style.marginRight); + } + return width; +}; + +var position = function position(element) { + return { + left: element.offsetLeft, + top: element.offsetTop + }; +}; + +var css = function css(element, obj) { + if (!obj) { + return window.getComputedStyle(element); + } + if (Object(__WEBPACK_IMPORTED_MODULE_0__type__["b" /* isObject */])(obj)) { + var style = ''; + Object.keys(obj).forEach(function (key) { + style += key + ': ' + obj[key] + ';'; + }); + + element.style.cssText += style; + } +}; + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (immutable) */ __webpack_exports__["a"] = detectSupportsPassive; +function detectSupportsPassive() { + var supportsPassive = false; + + try { + var opts = Object.defineProperty({}, 'passive', { + get: function get() { + supportsPassive = true; + } + }); + + window.addEventListener('testPassive', null, opts); + window.removeEventListener('testPassive', null, opts); + } catch (e) {} + + return supportsPassive; +} + +/***/ }), +/* 2 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isFunction; }); +/* unused harmony export isNumber */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "c", function() { return isString; }); +/* unused harmony export isDate */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return isObject; }); +/* unused harmony export isEmptyObject */ +/* unused harmony export isNode */ +/* unused harmony export isVideo */ +/* unused harmony export isHTML5 */ +/* unused harmony export isIFrame */ +/* unused harmony export isYoutube */ +/* unused harmony export isVimeo */ +var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) { return typeof obj; } : function (obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; + +var isFunction = function isFunction(unknown) { + return typeof unknown === 'function'; +}; +var isNumber = function isNumber(unknown) { + return typeof unknown === "number"; +}; +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; +var isDate = function isDate(unknown) { + return (Object.prototype.toString.call(unknown) === '[object Date]' || unknown instanceof Date) && !isNaN(unknown.valueOf()); +}; +var isObject = function isObject(unknown) { + return (typeof unknown === 'function' || (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && !!unknown) && !Array.isArray(unknown); +}; +var isEmptyObject = function isEmptyObject(unknown) { + for (var name in unknown) { + if (unknown.hasOwnProperty(name)) { + return false; + } + } + return true; +}; + +var isNode = function isNode(unknown) { + return !!(unknown && unknown.nodeType === HTMLElement | SVGElement); +}; +var isVideo = function isVideo(unknown) { + return isYoutube(unknown) || isVimeo(unknown) || isHTML5(unknown); +}; +var isHTML5 = function isHTML5(unknown) { + return isNode(unknown) && unknown.tagName === 'VIDEO'; +}; +var isIFrame = function isIFrame(unknown) { + return isNode(unknown) && unknown.tagName === 'IFRAME'; +}; +var isYoutube = function isYoutube(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/\/\/.*?youtube(-nocookie)?\.[a-z]+\/(watch\?v=[^&\s]+|embed)|youtu\.be\/.*/); +}; +var isVimeo = function isVimeo(unknown) { + return isIFrame(unknown) && !!unknown.src.match(/vimeo\.com\/video\/.*/); +}; + +/***/ }), +/* 3 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var events = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this.events = new Map(events); + } + + _createClass(EventEmitter, [{ + key: "on", + value: function on(name, cb) { + var _this = this; + + this.events.set(name, [].concat(_toConsumableArray(this.events.has(name) ? this.events.get(name) : []), [cb])); + + return function () { + return _this.events.set(name, _this.events.get(name).filter(function (fn) { + return fn !== cb; + })); + }; + } + }, { + key: "emit", + value: function emit(name) { + for (var _len = arguments.length, args = Array(_len > 1 ? _len - 1 : 0), _key = 1; _key < _len; _key++) { + args[_key - 1] = arguments[_key]; + } + + return this.events.has(name) && this.events.get(name).map(function (fn) { + return fn.apply(undefined, args); + }); + } + }]); + + return EventEmitter; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (EventEmitter); + +/***/ }), +/* 4 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Coordinate = function () { + function Coordinate() { + var x = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + var y = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : 0; + + _classCallCheck(this, Coordinate); + + this._x = x; + this._y = y; + } + + _createClass(Coordinate, [{ + key: 'add', + value: function add(coord) { + return new Coordinate(this._x + coord._x, this._y + coord._y); + } + }, { + key: 'sub', + value: function sub(coord) { + return new Coordinate(this._x - coord._x, this._y - coord._y); + } + }, { + key: 'distance', + value: function distance(coord) { + var deltaX = this._x - coord._x; + var deltaY = this._y - coord._y; + + return Math.sqrt(Math.pow(deltaX, 2) + Math.pow(deltaY, 2)); + } + }, { + key: 'max', + value: function max(coord) { + var x = Math.max(this._x, coord._x); + var y = Math.max(this._y, coord._y); + + return new Coordinate(x, y); + } + }, { + key: 'equals', + value: function equals(coord) { + if (this == coord) { + return true; + } + if (!coord || coord == null) { + return false; + } + return this._x == coord._x && this._y == coord._y; + } + }, { + key: 'inside', + value: function inside(northwest, southeast) { + if (this._x >= northwest._x && this._x <= southeast._x && this._y >= northwest._y && this._y <= southeast._y) { + + return true; + } + return false; + } + }, { + key: 'constrain', + value: function constrain(min, max) { + if (min._x > max._x || min._y > max._y) { + return this; + } + + var x = this._x, + y = this._y; + + if (min._x !== null) { + x = Math.max(x, min._x); + } + if (max._x !== null) { + x = Math.min(x, max._x); + } + if (min._y !== null) { + y = Math.max(y, min._y); + } + if (max._y !== null) { + y = Math.min(y, max._y); + } + + return new Coordinate(x, y); + } + }, { + key: 'reposition', + value: function reposition(element) { + element.style['top'] = this._y + 'px'; + element.style['left'] = this._x + 'px'; + } + }, { + key: 'toString', + value: function toString() { + return '(' + this._x + ',' + this._y + ')'; + } + }, { + key: 'x', + get: function get() { + return this._x; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._x = value; + return this; + } + }, { + key: 'y', + get: function get() { + return this._y; + }, + set: function set() { + var value = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + this._y = value; + return this; + } + }]); + + return Coordinate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Coordinate); + +/***/ }), +/* 5 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +Object.defineProperty(__webpack_exports__, "__esModule", { value: true }); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_index__ = __webpack_require__(6); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_type__ = __webpack_require__(2); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_4__components_autoplay__ = __webpack_require__(7); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__ = __webpack_require__(9); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_6__components_infinite__ = __webpack_require__(10); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_7__components_loop__ = __webpack_require__(11); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_8__components_navigation__ = __webpack_require__(13); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_9__components_pagination__ = __webpack_require__(15); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_10__components_swipe__ = __webpack_require__(18); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_11__components_transitioner__ = __webpack_require__(19); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_12__defaultOptions__ = __webpack_require__(22); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_13__templates__ = __webpack_require__(23); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_14__templates_item__ = __webpack_require__(24); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _defineProperty(obj, key, value) { if (key in obj) { Object.defineProperty(obj, key, { value: value, enumerable: true, configurable: true, writable: true }); } else { obj[key] = value; } return obj; } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + + + + + + + + + + + + + + + + +var bulmaCarousel = function (_EventEmitter) { + _inherits(bulmaCarousel, _EventEmitter); + + function bulmaCarousel(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaCarousel); + + var _this = _possibleConstructorReturn(this, (bulmaCarousel.__proto__ || Object.getPrototypeOf(bulmaCarousel)).call(this)); + + _this.element = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + _this._clickEvents = ['click', 'touch']; + + // Use Element dataset values to override options + var elementConfig = _this.element.dataset ? Object.keys(_this.element.dataset).filter(function (key) { + return Object.keys(__WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */]).includes(key); + }).reduce(function (obj, key) { + return _extends({}, obj, _defineProperty({}, key, _this.element.dataset[key])); + }, {}) : {}; + // Set default options - dataset attributes are master + _this.options = _extends({}, __WEBPACK_IMPORTED_MODULE_12__defaultOptions__["a" /* default */], options, elementConfig); + + _this._id = Object(__WEBPACK_IMPORTED_MODULE_0__utils_index__["a" /* uuid */])('slider'); + + _this.onShow = _this.onShow.bind(_this); + + // Initiate plugin + _this._init(); + return _this; + } + + /** + * Initiate all DOM element containing datePicker class + * @method + * @return {Array} Array of all datePicker instances + */ + + + _createClass(bulmaCarousel, [{ + key: '_init', + + + /**************************************************** + * * + * PRIVATE FUNCTIONS * + * * + ****************************************************/ + /** + * Initiate plugin instance + * @method _init + * @return {Slider} Current plugin instance + */ + value: function _init() { + this._items = Array.from(this.element.children); + + // Load plugins + this._breakpoint = new __WEBPACK_IMPORTED_MODULE_5__components_breakpoint__["a" /* default */](this); + this._autoplay = new __WEBPACK_IMPORTED_MODULE_4__components_autoplay__["a" /* default */](this); + this._navigation = new __WEBPACK_IMPORTED_MODULE_8__components_navigation__["a" /* default */](this); + this._pagination = new __WEBPACK_IMPORTED_MODULE_9__components_pagination__["a" /* default */](this); + this._infinite = new __WEBPACK_IMPORTED_MODULE_6__components_infinite__["a" /* default */](this); + this._loop = new __WEBPACK_IMPORTED_MODULE_7__components_loop__["a" /* default */](this); + this._swipe = new __WEBPACK_IMPORTED_MODULE_10__components_swipe__["a" /* default */](this); + + this._build(); + + if (Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["a" /* isFunction */])(this.options.onReady)) { + this.options.onReady(this); + } + + return this; + } + + /** + * Build Slider HTML component and append it to the DOM + * @method _build + */ + + }, { + key: '_build', + value: function _build() { + var _this2 = this; + + // Generate HTML Fragment of template + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_13__templates__["a" /* default */])(this.id)); + // Save pointers to template parts + this._ui = { + wrapper: this.node.firstChild, + container: this.node.querySelector('.slider-container') + + // Add slider to DOM + };this.element.appendChild(this.node); + this._ui.wrapper.classList.add('is-loading'); + this._ui.container.style.opacity = 0; + + this._transitioner = new __WEBPACK_IMPORTED_MODULE_11__components_transitioner__["a" /* default */](this); + + // Wrap all items by slide element + this._slides = this._items.map(function (item, index) { + return _this2._createSlide(item, index); + }); + + this.reset(); + + this._bindEvents(); + + this._ui.container.style.opacity = 1; + this._ui.wrapper.classList.remove('is-loading'); + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.on('show', this.onShow); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.off('show', this.onShow); + } + }, { + key: '_createSlide', + value: function _createSlide(item, index) { + var slide = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_14__templates_item__["a" /* default */])()).firstChild; + slide.dataset.sliderIndex = index; + slide.appendChild(item); + return slide; + } + + /** + * Calculate slider dimensions + */ + + }, { + key: '_setDimensions', + value: function _setDimensions() { + var _this3 = this; + + if (!this.options.vertical) { + if (this.options.centerMode) { + this._ui.wrapper.style.padding = '0px ' + this.options.centerPadding; + } + } else { + this._ui.wrapper.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this.slidesToShow; + if (this.options.centerMode) { + this._ui.wrapper.style.padding = this.options.centerPadding + ' 0px'; + } + } + + this._wrapperWidth = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this._ui.wrapper); + this._wrapperHeight = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._ui.wrapper); + + if (!this.options.vertical) { + this._slideWidth = Math.ceil(this._wrapperWidth / this.slidesToShow); + this._containerWidth = Math.ceil(this._slideWidth * this._slides.length); + this._ui.container.style.width = this._containerWidth + 'px'; + } else { + this._slideWidth = Math.ceil(this._wrapperWidth); + this._containerHeight = Math.ceil(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[0]) * this._slides.length); + this._ui.container.style.height = this._containerHeight + 'px'; + } + + this._slides.forEach(function (slide) { + slide.style.width = _this3._slideWidth + 'px'; + }); + } + }, { + key: '_setHeight', + value: function _setHeight() { + if (this.options.effect !== 'translate') { + this._ui.container.style.height = Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["c" /* outerHeight */])(this._slides[this.state.index]) + 'px'; + } + } + + // Update slides classes + + }, { + key: '_setClasses', + value: function _setClasses() { + var _this4 = this; + + this._slides.forEach(function (slide) { + Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["d" /* removeClasses */])(slide, 'is-active is-current is-slide-previous is-slide-next'); + if (Math.abs((_this4.state.index - 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-previous'); + } + if (Math.abs(_this4.state.index % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-current'); + } + if (Math.abs((_this4.state.index + 1) % _this4.state.length) === parseInt(slide.dataset.sliderIndex, 10)) { + slide.classList.add('is-slide-next'); + } + }); + } + + /**************************************************** + * * + * GETTERS and SETTERS * + * * + ****************************************************/ + + /** + * Get id of current datePicker + */ + + }, { + key: 'onShow', + + + /**************************************************** + * * + * EVENTS FUNCTIONS * + * * + ****************************************************/ + value: function onShow(e) { + this._navigation.refresh(); + this._pagination.refresh(); + this._setClasses(); + } + + /**************************************************** + * * + * PUBLIC FUNCTIONS * + * * + ****************************************************/ + + }, { + key: 'next', + value: function next() { + if (!this.options.loop && !this.options.infinite && this.state.index + this.slidesToScroll > this.state.length - this.slidesToShow && !this.options.centerMode) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index + this.slidesToScroll; + } + this.show(); + } + }, { + key: 'previous', + value: function previous() { + if (!this.options.loop && !this.options.infinite && this.state.index === 0) { + this.state.next = this.state.index; + } else { + this.state.next = this.state.index - this.slidesToScroll; + } + this.show(); + } + }, { + key: 'start', + value: function start() { + this._autoplay.start(); + } + }, { + key: 'pause', + value: function pause() { + this._autoplay.pause(); + } + }, { + key: 'stop', + value: function stop() { + this._autoplay.stop(); + } + }, { + key: 'show', + value: function show(index) { + var force = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + // If all slides are already visible then return + if (!this.state.length || this.state.length <= this.slidesToShow) { + return; + } + + if (typeof index === 'Number') { + this.state.next = index; + } + + if (this.options.loop) { + this._loop.apply(); + } + if (this.options.infinite) { + this._infinite.apply(); + } + + // If new slide is already the current one then return + if (this.state.index === this.state.next) { + return; + } + + this.emit('before:show', this.state); + this._transitioner.apply(force, this._setHeight.bind(this)); + this.emit('after:show', this.state); + + this.emit('show', this); + } + }, { + key: 'reset', + value: function reset() { + var _this5 = this; + + this.state = { + length: this._items.length, + index: Math.abs(this.options.initialSlide), + next: Math.abs(this.options.initialSlide), + prev: undefined + }; + + // Fix options + if (this.options.loop && this.options.infinite) { + this.options.loop = false; + } + if (this.options.slidesToScroll > this.options.slidesToShow) { + this.options.slidesToScroll = this.slidesToShow; + } + this._breakpoint.init(); + + if (this.state.index >= this.state.length && this.state.index !== 0) { + this.state.index = this.state.index - this.slidesToScroll; + } + if (this.state.length <= this.slidesToShow) { + this.state.index = 0; + } + + this._ui.wrapper.appendChild(this._navigation.init().render()); + this._ui.wrapper.appendChild(this._pagination.init().render()); + + if (this.options.navigationSwipe) { + this._swipe.bindEvents(); + } else { + this._swipe._bindEvents(); + } + + this._breakpoint.apply(); + // Move all created slides into slider + this._slides.forEach(function (slide) { + return _this5._ui.container.appendChild(slide); + }); + this._transitioner.init().apply(true, this._setHeight.bind(this)); + + if (this.options.autoplay) { + this._autoplay.init().start(); + } + } + + /** + * Destroy Slider + * @method destroy + */ + + }, { + key: 'destroy', + value: function destroy() { + var _this6 = this; + + this._unbindEvents(); + this._items.forEach(function (item) { + _this6.element.appendChild(item); + }); + this.node.remove(); + } + }, { + key: 'id', + get: function get() { + return this._id; + } + }, { + key: 'index', + set: function set(index) { + this._index = index; + }, + get: function get() { + return this._index; + } + }, { + key: 'length', + set: function set(length) { + this._length = length; + }, + get: function get() { + return this._length; + } + }, { + key: 'slides', + get: function get() { + return this._slides; + }, + set: function set(slides) { + this._slides = slides; + } + }, { + key: 'slidesToScroll', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToScroll() : 1; + } + }, { + key: 'slidesToShow', + get: function get() { + return this.options.effect === 'translate' ? this._breakpoint.getSlidesToShow() : 1; + } + }, { + key: 'direction', + get: function get() { + return this.element.dir.toLowerCase() === 'rtl' || this.element.style.direction === 'rtl' ? 'rtl' : 'ltr'; + } + }, { + key: 'wrapper', + get: function get() { + return this._ui.wrapper; + } + }, { + key: 'wrapperWidth', + get: function get() { + return this._wrapperWidth || 0; + } + }, { + key: 'container', + get: function get() { + return this._ui.container; + } + }, { + key: 'containerWidth', + get: function get() { + return this._containerWidth || 0; + } + }, { + key: 'slideWidth', + get: function get() { + return this._slideWidth || 0; + } + }, { + key: 'transitioner', + get: function get() { + return this._transitioner; + } + }], [{ + key: 'attach', + value: function attach() { + var _this7 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : '.slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = Object(__WEBPACK_IMPORTED_MODULE_2__utils_type__["c" /* isString */])(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + [].forEach.call(elements, function (element) { + if (typeof element[_this7.constructor.name] === 'undefined') { + var instance = new bulmaCarousel(element, options); + element[_this7.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this7.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaCarousel; +}(__WEBPACK_IMPORTED_MODULE_3__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaCarousel); + +/***/ }), +/* 6 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return uuid; }); +/* unused harmony export isRtl */ +/* unused harmony export defer */ +/* unused harmony export getNodeIndex */ +/* unused harmony export camelize */ +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +var uuid = function uuid() { + var prefix = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ''; + return prefix + ([1e7] + -1e3 + -4e3 + -8e3 + -1e11).replace(/[018]/g, function (c) { + return (c ^ crypto.getRandomValues(new Uint8Array(1))[0] & 15 >> c / 4).toString(16); + }); +}; +var isRtl = function isRtl() { + return document.documentElement.getAttribute('dir') === 'rtl'; +}; + +var defer = function defer() { + this.promise = new Promise(function (resolve, reject) { + this.resolve = resolve; + this.reject = reject; + }.bind(this)); + + this.then = this.promise.then.bind(this.promise); + this.catch = this.promise.catch.bind(this.promise); +}; + +var getNodeIndex = function getNodeIndex(node) { + return [].concat(_toConsumableArray(node.parentNode.children)).indexOf(node); +}; +var camelize = function camelize(str) { + return str.replace(/-(\w)/g, toUpper); +}; + +/***/ }), +/* 7 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__ = __webpack_require__(3); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_device__ = __webpack_require__(8); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + + +var onVisibilityChange = Symbol('onVisibilityChange'); +var onMouseEnter = Symbol('onMouseEnter'); +var onMouseLeave = Symbol('onMouseLeave'); + +var defaultOptions = { + autoplay: false, + autoplaySpeed: 3000 +}; + +var Autoplay = function (_EventEmitter) { + _inherits(Autoplay, _EventEmitter); + + function Autoplay(slider) { + _classCallCheck(this, Autoplay); + + var _this = _possibleConstructorReturn(this, (Autoplay.__proto__ || Object.getPrototypeOf(Autoplay)).call(this)); + + _this.slider = slider; + + _this.onVisibilityChange = _this.onVisibilityChange.bind(_this); + _this.onMouseEnter = _this.onMouseEnter.bind(_this); + _this.onMouseLeave = _this.onMouseLeave.bind(_this); + return _this; + } + + _createClass(Autoplay, [{ + key: 'init', + value: function init() { + this._bindEvents(); + return this; + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + document.addEventListener('visibilitychange', this.onVisibilityChange); + if (this.slider.options.pauseOnHover) { + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.addEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + document.removeEventListener('visibilitychange', this.onVisibilityChange); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["a" /* pointerEnter */], this.onMouseEnter); + this.slider.container.removeEventListener(__WEBPACK_IMPORTED_MODULE_1__utils_device__["b" /* pointerLeave */], this.onMouseLeave); + } + }, { + key: 'start', + value: function start() { + var _this2 = this; + + this.stop(); + if (this.slider.options.autoplay) { + this.emit('start', this); + this._interval = setInterval(function () { + if (!(_this2._hovering && _this2.slider.options.pauseOnHover)) { + if (!_this2.slider.options.centerMode && _this2.slider.state.next >= _this2.slider.state.length - _this2.slider.slidesToShow && !_this2.slider.options.loop && !_this2.slider.options.infinite) { + _this2.stop(); + } else { + _this2.slider.next(); + } + } + }, this.slider.options.autoplaySpeed); + } + } + }, { + key: 'stop', + value: function stop() { + this._interval = clearInterval(this._interval); + this.emit('stop', this); + } + }, { + key: 'pause', + value: function pause() { + var _this3 = this; + + var speed = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 0; + + if (this.paused) { + return; + } + if (this.timer) { + this.stop(); + } + this.paused = true; + if (speed === 0) { + this.paused = false; + this.start(); + } else { + this.slider.on('transition:end', function () { + if (!_this3) { + return; + } + _this3.paused = false; + if (!_this3.run) { + _this3.stop(); + } else { + _this3.start(); + } + }); + } + } + }, { + key: 'onVisibilityChange', + value: function onVisibilityChange(e) { + if (document.hidden) { + this.stop(); + } else { + this.start(); + } + } + }, { + key: 'onMouseEnter', + value: function onMouseEnter(e) { + this._hovering = true; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }, { + key: 'onMouseLeave', + value: function onMouseLeave(e) { + this._hovering = false; + if (this.slider.options.pauseOnHover) { + this.pause(); + } + } + }]); + + return Autoplay; +}(__WEBPACK_IMPORTED_MODULE_0__utils_eventEmitter__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["a"] = (Autoplay); + +/***/ }), +/* 8 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* unused harmony export isIE */ +/* unused harmony export isIETouch */ +/* unused harmony export isAndroid */ +/* unused harmony export isiPad */ +/* unused harmony export isiPod */ +/* unused harmony export isiPhone */ +/* unused harmony export isSafari */ +/* unused harmony export isUiWebView */ +/* unused harmony export supportsTouchEvents */ +/* unused harmony export supportsPointerEvents */ +/* unused harmony export supportsTouch */ +/* unused harmony export pointerDown */ +/* unused harmony export pointerMove */ +/* unused harmony export pointerUp */ +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return pointerEnter; }); +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "b", function() { return pointerLeave; }); +var isIE = window.navigator.pointerEnabled || window.navigator.msPointerEnabled; +var isIETouch = window.navigator.msPointerEnabled && window.navigator.msMaxTouchPoints > 1 || window.navigator.pointerEnabled && window.navigator.maxTouchPoints > 1; +var isAndroid = navigator.userAgent.match(/(Android);?[\s\/]+([\d.]+)?/); +var isiPad = navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/); +var isiPod = navigator.userAgent.match(/(iPod)(.*OS\s([\d_]+))?/); +var isiPhone = !navigator.userAgent.match(/(iPad).*OS\s([\d_]+)/) && navigator.userAgent.match(/(iPhone\sOS)\s([\d_]+)/); +var isSafari = navigator.userAgent.toLowerCase().indexOf('safari') >= 0 && navigator.userAgent.toLowerCase().indexOf('chrome') < 0 && navigator.userAgent.toLowerCase().indexOf('android') < 0; +var isUiWebView = /(iPhone|iPod|iPad).*AppleWebKit(?!.*Safari)/i.test(navigator.userAgent); + +var supportsTouchEvents = !!('ontouchstart' in window); +var supportsPointerEvents = !!('PointerEvent' in window); +var supportsTouch = supportsTouchEvents || window.DocumentTouch && document instanceof DocumentTouch || navigator.maxTouchPoints; // IE >=11 +var pointerDown = !supportsTouch ? 'mousedown' : 'mousedown ' + (supportsTouchEvents ? 'touchstart' : 'pointerdown'); +var pointerMove = !supportsTouch ? 'mousemove' : 'mousemove ' + (supportsTouchEvents ? 'touchmove' : 'pointermove'); +var pointerUp = !supportsTouch ? 'mouseup' : 'mouseup ' + (supportsTouchEvents ? 'touchend' : 'pointerup'); +var pointerEnter = supportsTouch && supportsPointerEvents ? 'pointerenter' : 'mouseenter'; +var pointerLeave = supportsTouch && supportsPointerEvents ? 'pointerleave' : 'mouseleave'; + +/***/ }), +/* 9 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var onResize = Symbol('onResize'); + +var Breakpoints = function () { + function Breakpoints(slider) { + _classCallCheck(this, Breakpoints); + + this.slider = slider; + this.options = slider.options; + + this[onResize] = this[onResize].bind(this); + + this._bindEvents(); + } + + _createClass(Breakpoints, [{ + key: 'init', + value: function init() { + this._defaultBreakpoint = { + slidesToShow: this.options.slidesToShow, + slidesToScroll: this.options.slidesToScroll + }; + this.options.breakpoints.sort(function (a, b) { + return parseInt(a.changePoint, 10) > parseInt(b.changePoint, 10); + }); + this._currentBreakpoint = this._getActiveBreakpoint(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + window.addEventListener('resize', this[onResize]); + window.addEventListener('orientationchange', this[onResize]); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + window.removeEventListener('resize', this[onResize]); + window.removeEventListener('orientationchange', this[onResize]); + } + }, { + key: '_getActiveBreakpoint', + value: function _getActiveBreakpoint() { + //Get breakpoint for window width + var _iteratorNormalCompletion = true; + var _didIteratorError = false; + var _iteratorError = undefined; + + try { + for (var _iterator = this.options.breakpoints[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { + var point = _step.value; + + if (point.changePoint >= window.innerWidth) { + return point; + } + } + } catch (err) { + _didIteratorError = true; + _iteratorError = err; + } finally { + try { + if (!_iteratorNormalCompletion && _iterator.return) { + _iterator.return(); + } + } finally { + if (_didIteratorError) { + throw _iteratorError; + } + } + } + + return this._defaultBreakpoint; + } + }, { + key: 'getSlidesToShow', + value: function getSlidesToShow() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToShow : this._defaultBreakpoint.slidesToShow; + } + }, { + key: 'getSlidesToScroll', + value: function getSlidesToScroll() { + return this._currentBreakpoint ? this._currentBreakpoint.slidesToScroll : this._defaultBreakpoint.slidesToScroll; + } + }, { + key: 'apply', + value: function apply() { + if (this.slider.state.index >= this.slider.state.length && this.slider.state.index !== 0) { + this.slider.state.index = this.slider.state.index - this._currentBreakpoint.slidesToScroll; + } + if (this.slider.state.length <= this._currentBreakpoint.slidesToShow) { + this.slider.state.index = 0; + } + + if (this.options.loop) { + this.slider._loop.init().apply(); + } + + if (this.options.infinite) { + this.slider._infinite.init().apply(); + } + + this.slider._setDimensions(); + this.slider._transitioner.init().apply(true, this.slider._setHeight.bind(this.slider)); + this.slider._setClasses(); + + this.slider._navigation.refresh(); + this.slider._pagination.refresh(); + } + }, { + key: onResize, + value: function value(e) { + var newBreakPoint = this._getActiveBreakpoint(); + if (newBreakPoint.slidesToShow !== this._currentBreakpoint.slidesToShow) { + this._currentBreakpoint = newBreakPoint; + this.apply(); + } + } + }]); + + return Breakpoints; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Breakpoints); + +/***/ }), +/* 10 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _toConsumableArray(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } else { return Array.from(arr); } } + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var Infinite = function () { + function Infinite(slider) { + _classCallCheck(this, Infinite); + + this.slider = slider; + } + + _createClass(Infinite, [{ + key: 'init', + value: function init() { + if (this.slider.options.infinite && this.slider.options.effect === 'translate') { + if (this.slider.options.centerMode) { + this._infiniteCount = Math.ceil(this.slider.slidesToShow + this.slider.slidesToShow / 2); + } else { + this._infiniteCount = this.slider.slidesToShow; + } + + var frontClones = []; + var slideIndex = 0; + for (var i = this.slider.state.length; i > this.slider.state.length - 1 - this._infiniteCount; i -= 1) { + slideIndex = i - 1; + frontClones.unshift(this._cloneSlide(this.slider.slides[slideIndex], slideIndex - this.slider.state.length)); + } + + var backClones = []; + for (var _i = 0; _i < this._infiniteCount + this.slider.state.length; _i += 1) { + backClones.push(this._cloneSlide(this.slider.slides[_i % this.slider.state.length], _i + this.slider.state.length)); + } + + this.slider.slides = [].concat(frontClones, _toConsumableArray(this.slider.slides), backClones); + } + return this; + } + }, { + key: 'apply', + value: function apply() {} + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.slider.options.infinite) { + if (this.slider.state.next >= this.slider.state.length) { + this.slider.state.index = this.slider.state.next = this.slider.state.next - this.slider.state.length; + this.slider.transitioner.apply(true); + } else if (this.slider.state.next < 0) { + this.slider.state.index = this.slider.state.next = this.slider.state.length + this.slider.state.next; + this.slider.transitioner.apply(true); + } + } + } + }, { + key: '_cloneSlide', + value: function _cloneSlide(slide, index) { + var newSlide = slide.cloneNode(true); + newSlide.dataset.sliderIndex = index; + newSlide.dataset.cloned = true; + var ids = newSlide.querySelectorAll('[id]') || []; + ids.forEach(function (id) { + id.setAttribute('id', ''); + }); + return newSlide; + } + }]); + + return Infinite; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Infinite); + +/***/ }), +/* 11 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_dom__ = __webpack_require__(12); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Loop = function () { + function Loop(slider) { + _classCallCheck(this, Loop); + + this.slider = slider; + } + + _createClass(Loop, [{ + key: "init", + value: function init() { + return this; + } + }, { + key: "apply", + value: function apply() { + if (this.slider.options.loop) { + if (this.slider.state.next > 0) { + if (this.slider.state.next < this.slider.state.length) { + if (this.slider.state.next > this.slider.state.length - this.slider.slidesToShow && Object(__WEBPACK_IMPORTED_MODULE_0__utils_dom__["a" /* isInViewport */])(this.slider._slides[this.slider.state.length - 1], this.slider.wrapper)) { + this.slider.state.next = 0; + } else { + this.slider.state.next = Math.min(Math.max(this.slider.state.next, 0), this.slider.state.length - this.slider.slidesToShow); + } + } else { + this.slider.state.next = 0; + } + } else { + if (this.slider.state.next <= 0 - this.slider.slidesToScroll) { + this.slider.state.next = this.slider.state.length - this.slider.slidesToShow; + } else { + this.slider.state.next = 0; + } + } + } + } + }]); + + return Loop; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Loop); + +/***/ }), +/* 12 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony export (binding) */ __webpack_require__.d(__webpack_exports__, "a", function() { return isInViewport; }); +var isInViewport = function isInViewport(element, html) { + var rect = element.getBoundingClientRect(); + html = html || document.documentElement; + return rect.top >= 0 && rect.left >= 0 && rect.bottom <= (window.innerHeight || html.clientHeight) && rect.right <= (window.innerWidth || html.clientWidth); +}; + +/***/ }), +/* 13 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_navigation__ = __webpack_require__(14); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Navigation = function () { + function Navigation(slider) { + _classCallCheck(this, Navigation); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPreviousClick = this.onPreviousClick.bind(this); + this.onNextClick = this.onNextClick.bind(this); + this.onKeyUp = this.onKeyUp.bind(this); + } + + _createClass(Navigation, [{ + key: 'init', + value: function init() { + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_navigation__["a" /* default */])(this.slider.options.icons)); + this._ui = { + previous: this.node.querySelector('.slider-navigation-previous'), + next: this.node.querySelector('.slider-navigation-next') + }; + + this._unbindEvents(); + this._bindEvents(); + + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + this.slider.wrapper.addEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this._ui.previous.addEventListener(clickEvent, _this.onPreviousClick); + _this._ui.next.addEventListener(clickEvent, _this.onNextClick); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + this.slider.wrapper.removeEventListener('keyup', this.onKeyUp); + this._clickEvents.forEach(function (clickEvent) { + _this2._ui.previous.removeEventListener(clickEvent, _this2.onPreviousClick); + _this2._ui.next.removeEventListener(clickEvent, _this2.onNextClick); + }); + } + }, { + key: 'onNextClick', + value: function onNextClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.next(); + } + } + }, { + key: 'onPreviousClick', + value: function onPreviousClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + if (this.slider.options.navigation) { + this.slider.previous(); + } + } + }, { + key: 'onKeyUp', + value: function onKeyUp(e) { + if (this.slider.options.keyNavigation) { + if (e.key === 'ArrowRight' || e.key === 'Right') { + this.slider.next(); + } else if (e.key === 'ArrowLeft' || e.key === 'Left') { + this.slider.previous(); + } + } + } + }, { + key: 'refresh', + value: function refresh() { + // let centerOffset = Math.floor(this.options.slidesToShow / 2); + if (!this.slider.options.loop && !this.slider.options.infinite) { + if (this.slider.options.navigation && this.slider.state.length > this.slider.slidesToShow) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + if (this.slider.state.next === 0) { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.remove('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - this.slider.slidesToShow && !this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } else if (this.slider.state.next >= this.slider.state.length - 1 && this.slider.options.centerMode) { + this._ui.previous.classList.remove('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } else { + this._ui.previous.classList.add('is-hidden'); + this._ui.next.classList.add('is-hidden'); + } + } + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Navigation; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Navigation); + +/***/ }), +/* 14 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (icons) { + return "
" + icons.previous + "
\n
" + icons.next + "
"; +}); + +/***/ }), +/* 15 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__templates_pagination__ = __webpack_require__(16); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__ = __webpack_require__(17); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + + +var Pagination = function () { + function Pagination(slider) { + _classCallCheck(this, Pagination); + + this.slider = slider; + + this._clickEvents = ['click', 'touch']; + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_2__utils_detect_supportsPassive__["a" /* default */])(); + + this.onPageClick = this.onPageClick.bind(this); + this.onResize = this.onResize.bind(this); + } + + _createClass(Pagination, [{ + key: 'init', + value: function init() { + this._pages = []; + this.node = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_0__templates_pagination__["a" /* default */])()); + this._ui = { + container: this.node.firstChild + }; + + this._count = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + + this._draw(); + this.refresh(); + + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + var _this = this; + + window.addEventListener('resize', this.onResize); + window.addEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this._pages.forEach(function (page) { + return page.addEventListener(clickEvent, _this.onPageClick); + }); + }); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + var _this2 = this; + + window.removeEventListener('resize', this.onResize); + window.removeEventListener('orientationchange', this.onResize); + + this._clickEvents.forEach(function (clickEvent) { + _this2._pages.forEach(function (page) { + return page.removeEventListener(clickEvent, _this2.onPageClick); + }); + }); + } + }, { + key: '_draw', + value: function _draw() { + this._ui.container.innerHTML = ''; + if (this.slider.options.pagination && this.slider.state.length > this.slider.slidesToShow) { + for (var i = 0; i <= this._count; i++) { + var newPageNode = document.createRange().createContextualFragment(Object(__WEBPACK_IMPORTED_MODULE_1__templates_pagination_page__["a" /* default */])()).firstChild; + newPageNode.dataset.index = i * this.slider.slidesToScroll; + this._pages.push(newPageNode); + this._ui.container.appendChild(newPageNode); + } + this._bindEvents(); + } + } + }, { + key: 'onPageClick', + value: function onPageClick(e) { + if (!this._supportsPassive) { + e.preventDefault(); + } + + this.slider.state.next = e.currentTarget.dataset.index; + this.slider.show(); + } + }, { + key: 'onResize', + value: function onResize() { + this._draw(); + } + }, { + key: 'refresh', + value: function refresh() { + var _this3 = this; + + var newCount = void 0; + + if (this.slider.options.infinite) { + newCount = Math.ceil(this.slider.state.length - 1 / this.slider.slidesToScroll); + } else { + newCount = Math.ceil((this.slider.state.length - this.slider.slidesToShow) / this.slider.slidesToScroll); + } + if (newCount !== this._count) { + this._count = newCount; + this._draw(); + } + + this._pages.forEach(function (page) { + page.classList.remove('is-active'); + if (parseInt(page.dataset.index, 10) === _this3.slider.state.next % _this3.slider.state.length) { + page.classList.add('is-active'); + } + }); + } + }, { + key: 'render', + value: function render() { + return this.node; + } + }]); + + return Pagination; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Pagination); + +/***/ }), +/* 16 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 17 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }), +/* 18 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__ = __webpack_require__(1); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Swipe = function () { + function Swipe(slider) { + _classCallCheck(this, Swipe); + + this.slider = slider; + + this._supportsPassive = Object(__WEBPACK_IMPORTED_MODULE_1__utils_detect_supportsPassive__["a" /* default */])(); + + this.onStartDrag = this.onStartDrag.bind(this); + this.onMoveDrag = this.onMoveDrag.bind(this); + this.onStopDrag = this.onStopDrag.bind(this); + + this._init(); + } + + _createClass(Swipe, [{ + key: '_init', + value: function _init() {} + }, { + key: 'bindEvents', + value: function bindEvents() { + var _this = this; + + this.slider.container.addEventListener('dragstart', function (e) { + if (!_this._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.addEventListener('mousedown', this.onStartDrag); + this.slider.container.addEventListener('touchstart', this.onStartDrag); + + window.addEventListener('mousemove', this.onMoveDrag); + window.addEventListener('touchmove', this.onMoveDrag); + + window.addEventListener('mouseup', this.onStopDrag); + window.addEventListener('touchend', this.onStopDrag); + window.addEventListener('touchcancel', this.onStopDrag); + } + }, { + key: 'unbindEvents', + value: function unbindEvents() { + var _this2 = this; + + this.slider.container.removeEventListener('dragstart', function (e) { + if (!_this2._supportsPassive) { + e.preventDefault(); + } + }); + this.slider.container.removeEventListener('mousedown', this.onStartDrag); + this.slider.container.removeEventListener('touchstart', this.onStartDrag); + + window.removeEventListener('mousemove', this.onMoveDrag); + window.removeEventListener('touchmove', this.onMoveDrag); + + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('mouseup', this.onStopDrag); + window.removeEventListener('touchcancel', this.onStopDrag); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStartDrag', + value: function onStartDrag(e) { + if (e.touches) { + if (e.touches.length > 1) { + return; + } else { + e = e.touches[0]; + } + } + + this._origin = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](e.screenX, e.screenY); + this.width = this.slider.wrapperWidth; + this.slider.transitioner.disable(); + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onMoveDrag', + value: function onMoveDrag(e) { + if (this._origin) { + var point = e.touches ? e.touches[0] : e; + this._lastTranslate = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](point.screenX - this._origin.x, point.screenY - this._origin.y); + if (e.touches) { + if (Math.abs(this._lastTranslate.x) > Math.abs(this._lastTranslate.y)) { + if (!this._supportsPassive) { + e.preventDefault(); + } + e.stopPropagation(); + } + } + } + } + + /** + * @param {MouseEvent|TouchEvent} + */ + + }, { + key: 'onStopDrag', + value: function onStopDrag(e) { + if (this._origin && this._lastTranslate) { + if (Math.abs(this._lastTranslate.x) > 0.2 * this.width) { + if (this._lastTranslate.x < 0) { + this.slider.next(); + } else { + this.slider.previous(); + } + } else { + this.slider.show(true); + } + } + this._origin = null; + this._lastTranslate = null; + } + }]); + + return Swipe; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Swipe); + +/***/ }), +/* 19 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__transitions_fade__ = __webpack_require__(20); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__transitions_translate__ = __webpack_require__(21); +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Transitioner = function () { + function Transitioner(slider) { + _classCallCheck(this, Transitioner); + + this.slider = slider; + this.options = slider.options; + + this._animating = false; + this._animation = undefined; + + this._translate = new __WEBPACK_IMPORTED_MODULE_1__transitions_translate__["a" /* default */](this, slider, slider.options); + this._fade = new __WEBPACK_IMPORTED_MODULE_0__transitions_fade__["a" /* default */](this, slider, slider.options); + } + + _createClass(Transitioner, [{ + key: 'init', + value: function init() { + this._fade.init(); + this._translate.init(); + return this; + } + }, { + key: 'isAnimating', + value: function isAnimating() { + return this._animating; + } + }, { + key: 'enable', + value: function enable() { + this._animation && this._animation.enable(); + } + }, { + key: 'disable', + value: function disable() { + this._animation && this._animation.disable(); + } + }, { + key: 'apply', + value: function apply(force, callback) { + // If we don't force refresh and animation in progress then return + if (this._animating && !force) { + return; + } + + switch (this.options.effect) { + case 'fade': + this._animation = this._fade; + break; + case 'translate': + default: + this._animation = this._translate; + break; + } + + this._animationCallback = callback; + + if (force) { + this._animation && this._animation.disable(); + } else { + this._animation && this._animation.enable(); + this._animating = true; + } + + this._animation && this._animation.apply(); + + if (force) { + this.end(); + } + } + }, { + key: 'end', + value: function end() { + this._animating = false; + this._animation = undefined; + this.slider.state.index = this.slider.state.next; + if (this._animationCallback) { + this._animationCallback(); + } + } + }]); + + return Transitioner; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Transitioner); + +/***/ }), +/* 20 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + +var Fade = function () { + function Fade(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Fade); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + } + + _createClass(Fade, [{ + key: 'init', + value: function init() { + var _this = this; + + if (this.options.effect === 'fade') { + this.slider.slides.forEach(function (slide, index) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(slide, { + position: 'absolute', + left: 0, + top: 0, + bottom: 0, + 'z-index': slide.dataset.sliderIndex == _this.slider.state.index ? 0 : -2, + opacity: slide.dataset.sliderIndex == _this.slider.state.index ? 1 : 0 + }); + }); + } + return this; + } + }, { + key: 'enable', + value: function enable() { + var _this2 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this2.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + if (this._oldSlide) { + this._oldSlide.addEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + } + } + }, { + key: 'disable', + value: function disable() { + var _this3 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this3.slider.state.next; + })[0]; + if (this._newSlide) { + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._newSlide.style.transition = 'none'; + if (this._oldSlide) { + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + this._oldSlide.style.transition = 'none'; + } + } + } + }, { + key: 'apply', + value: function apply(force) { + var _this4 = this; + + this._oldSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.index; + })[0]; + this._newSlide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this4.slider.state.next; + })[0]; + + if (this._oldSlide && this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + opacity: 0 + }); + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + opacity: 1, + 'z-index': force ? 0 : -1 + }); + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'fade') { + if (this.transitioner.isAnimating() && e.target == this._newSlide) { + if (this._newSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._newSlide, { + 'z-index': 0 + }); + this._newSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + if (this._oldSlide) { + Object(__WEBPACK_IMPORTED_MODULE_0__utils_css__["a" /* css */])(this._oldSlide, { + 'z-index': -2 + }); + this._oldSlide.removeEventListener('transitionend', this.onTransitionEnd.bind(this)); + } + } + this.transitioner.end(); + } + } + }]); + + return Fade; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Fade); + +/***/ }), +/* 21 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__ = __webpack_require__(4); +/* harmony import */ var __WEBPACK_IMPORTED_MODULE_1__utils_css__ = __webpack_require__(0); +var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; + +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + + + + +var Translate = function () { + function Translate(transitioner, slider) { + var options = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : {}; + + _classCallCheck(this, Translate); + + this.transitioner = transitioner; + this.slider = slider; + this.options = _extends({}, options); + + this.onTransitionEnd = this.onTransitionEnd.bind(this); + } + + _createClass(Translate, [{ + key: 'init', + value: function init() { + this._position = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](this.slider.container.offsetLeft, this.slider.container.offsetTop); + this._bindEvents(); + return this; + } + }, { + key: 'destroy', + value: function destroy() { + this._unbindEvents(); + } + }, { + key: '_bindEvents', + value: function _bindEvents() { + this.slider.container.addEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: '_unbindEvents', + value: function _unbindEvents() { + this.slider.container.removeEventListener('transitionend', this.onTransitionEnd); + } + }, { + key: 'enable', + value: function enable() { + this.slider.container.style.transition = this.options.duration + 'ms ' + this.options.timing; + } + }, { + key: 'disable', + value: function disable() { + this.slider.container.style.transition = 'none'; + } + }, { + key: 'apply', + value: function apply() { + var _this = this; + + var maxOffset = void 0; + if (this.options.effect === 'translate') { + var slide = this.slider.slides.filter(function (slide) { + return slide.dataset.sliderIndex == _this.slider.state.next; + })[0]; + var slideOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](slide.offsetLeft, slide.offsetTop); + if (this.options.centerMode) { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container))); + } else { + maxOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(this.slider.wrapper)), Math.round(Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.container) - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["b" /* height */])(this.slider.wrapper))); + } + var nextOffset = new __WEBPACK_IMPORTED_MODULE_0__utils_coordinate__["a" /* default */](Math.min(Math.max(slideOffset.x * -1, maxOffset.x * -1), 0), Math.min(Math.max(slideOffset.y * -1, maxOffset.y * -1), 0)); + if (this.options.loop) { + if (!this.options.vertical && Math.abs(this._position.x) > maxOffset.x) { + nextOffset.x = 0; + this.slider.state.next = 0; + } else if (this.options.vertical && Math.abs(this._position.y) > maxOffset.y) { + nextOffset.y = 0; + this.slider.state.next = 0; + } + } + + this._position.x = nextOffset.x; + this._position.y = nextOffset.y; + if (this.options.centerMode) { + this._position.x = this._position.x + this.slider.wrapperWidth / 2 - Object(__WEBPACK_IMPORTED_MODULE_1__utils_css__["e" /* width */])(slide) / 2; + } + + if (this.slider.direction === 'rtl') { + this._position.x = -this._position.x; + this._position.y = -this._position.y; + } + this.slider.container.style.transform = 'translate3d(' + this._position.x + 'px, ' + this._position.y + 'px, 0)'; + + /** + * update the index with the nextIndex only if + * the offset of the nextIndex is in the range of the maxOffset + */ + if (slideOffset.x > maxOffset.x) { + this.slider.transitioner.end(); + } + } + } + }, { + key: 'onTransitionEnd', + value: function onTransitionEnd(e) { + if (this.options.effect === 'translate') { + + if (this.transitioner.isAnimating() && e.target == this.slider.container) { + if (this.options.infinite) { + this.slider._infinite.onTransitionEnd(e); + } + } + this.transitioner.end(); + } + } + }]); + + return Translate; +}(); + +/* harmony default export */ __webpack_exports__["a"] = (Translate); + +/***/ }), +/* 22 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var defaultOptions = { + initialSlide: 0, + slidesToScroll: 1, + slidesToShow: 1, + + navigation: true, + navigationKeys: true, + navigationSwipe: true, + + pagination: true, + + loop: false, + infinite: false, + + effect: 'translate', + duration: 300, + timing: 'ease', + + autoplay: false, + autoplaySpeed: 3000, + pauseOnHover: true, + breakpoints: [{ + changePoint: 480, + slidesToShow: 1, + slidesToScroll: 1 + }, { + changePoint: 640, + slidesToShow: 2, + slidesToScroll: 2 + }, { + changePoint: 768, + slidesToShow: 3, + slidesToScroll: 3 + }], + + onReady: null, + icons: { + 'previous': '\n \n ', + 'next': '\n \n ' + } +}; + +/* harmony default export */ __webpack_exports__["a"] = (defaultOptions); + +/***/ }), +/* 23 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function (id) { + return "
\n
\n
"; +}); + +/***/ }), +/* 24 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +/* harmony default export */ __webpack_exports__["a"] = (function () { + return "
"; +}); + +/***/ }) +/******/ ])["default"]; +}); diff --git a/docs/project_page/static/js/bulma-carousel.min.js b/docs/project_page/static/js/bulma-carousel.min.js new file mode 100644 index 0000000..f7cea5a --- /dev/null +++ b/docs/project_page/static/js/bulma-carousel.min.js @@ -0,0 +1 @@ +!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.bulmaCarousel=e():t.bulmaCarousel=e()}("undefined"!=typeof self?self:this,function(){return function(i){var n={};function s(t){if(n[t])return n[t].exports;var e=n[t]={i:t,l:!1,exports:{}};return i[t].call(e.exports,e,e.exports,s),e.l=!0,e.exports}return s.m=i,s.c=n,s.d=function(t,e,i){s.o(t,e)||Object.defineProperty(t,e,{configurable:!1,enumerable:!0,get:i})},s.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return s.d(e,"a",e),e},s.o=function(t,e){return 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e=1this.options.slidesToShow&&(this.options.slidesToScroll=this.slidesToShow),this._breakpoint.init(),this.state.index>=this.state.length&&0!==this.state.index&&(this.state.index=this.state.index-this.slidesToScroll),this.state.length<=this.slidesToShow&&(this.state.index=0),this._ui.wrapper.appendChild(this._navigation.init().render()),this._ui.wrapper.appendChild(this._pagination.init().render()),this.options.navigationSwipe?this._swipe.bindEvents():this._swipe._bindEvents(),this._breakpoint.apply(),this._slides.forEach(function(t){return e._ui.container.appendChild(t)}),this._transitioner.init().apply(!0,this._setHeight.bind(this)),this.options.autoplay&&this._autoplay.init().start()}},{key:"destroy",value:function(){var e=this;this._unbindEvents(),this._items.forEach(function(t){e.element.appendChild(t)}),this.node.remove()}},{key:"id",get:function(){return this._id}},{key:"index",set:function(t){this._index=t},get:function(){return 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"symbol" : typeof obj; }; + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; } + +function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } + + + +var isString = function isString(unknown) { + return typeof unknown === 'string' || !!unknown && (typeof unknown === 'undefined' ? 'undefined' : _typeof(unknown)) === 'object' && Object.prototype.toString.call(unknown) === '[object String]'; +}; + +var bulmaSlider = function (_EventEmitter) { + _inherits(bulmaSlider, _EventEmitter); + + function bulmaSlider(selector) { + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + _classCallCheck(this, bulmaSlider); + + var _this = _possibleConstructorReturn(this, (bulmaSlider.__proto__ || Object.getPrototypeOf(bulmaSlider)).call(this)); + + _this.element = typeof selector === 'string' ? document.querySelector(selector) : selector; + // An invalid selector or non-DOM node has been provided. + if (!_this.element) { + throw new Error('An invalid selector or non-DOM node has been provided.'); + } + + _this._clickEvents = ['click']; + /// Set default options and merge with instance defined + _this.options = _extends({}, options); + + _this.onSliderInput = _this.onSliderInput.bind(_this); + + _this.init(); + return _this; + } + + /** + * Initiate all DOM element containing selector + * @method + * @return {Array} Array of all slider instances + */ + + + _createClass(bulmaSlider, [{ + key: 'init', + + + /** + * Initiate plugin + * @method init + * @return {void} + */ + value: function init() { + this._id = 'bulmaSlider' + new Date().getTime() + Math.floor(Math.random() * Math.floor(9999)); + this.output = this._findOutputForSlider(); + + this._bindEvents(); + + if (this.output) { + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + } + + this.emit('bulmaslider:ready', this.element.value); + } + }, { + key: '_findOutputForSlider', + value: function _findOutputForSlider() { + var _this2 = this; + + var result = null; + var outputs = document.getElementsByTagName('output') || []; + + Array.from(outputs).forEach(function (output) { + if (output.htmlFor == _this2.element.getAttribute('id')) { + result = output; + return true; + } + }); + return result; + } + }, { + key: '_getSliderOutputPosition', + value: function _getSliderOutputPosition() { + // Update output position + var newPlace, minValue; + + var style = window.getComputedStyle(this.element, null); + // Measure width of range input + var sliderWidth = parseInt(style.getPropertyValue('width'), 10); + + // Figure out placement percentage between left and right of input + if (!this.element.getAttribute('min')) { + minValue = 0; + } else { + minValue = this.element.getAttribute('min'); + } + var newPoint = (this.element.value - minValue) / (this.element.getAttribute('max') - minValue); + + // Prevent bubble from going beyond left or right (unsupported browsers) + if (newPoint < 0) { + newPlace = 0; + } else if (newPoint > 1) { + newPlace = sliderWidth; + } else { + newPlace = sliderWidth * newPoint; + } + + return { + 'position': newPlace + 'px' + }; + } + + /** + * Bind all events + * @method _bindEvents + * @return {void} + */ + + }, { + key: '_bindEvents', + value: function _bindEvents() { + if (this.output) { + // Add event listener to update output when slider value change + this.element.addEventListener('input', this.onSliderInput, false); + } + } + }, { + key: 'onSliderInput', + value: function onSliderInput(e) { + e.preventDefault(); + + if (this.element.classList.contains('has-output-tooltip')) { + // Get new output position + var newPosition = this._getSliderOutputPosition(); + + // Set output position + this.output.style['left'] = newPosition.position; + } + + // Check for prefix and postfix + var prefix = this.output.hasAttribute('data-prefix') ? this.output.getAttribute('data-prefix') : ''; + var postfix = this.output.hasAttribute('data-postfix') ? this.output.getAttribute('data-postfix') : ''; + + // Update output with slider value + this.output.value = prefix + this.element.value + postfix; + + this.emit('bulmaslider:ready', this.element.value); + } + }], [{ + key: 'attach', + value: function attach() { + var _this3 = this; + + var selector = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 'input[type="range"].slider'; + var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {}; + + var instances = new Array(); + + var elements = isString(selector) ? document.querySelectorAll(selector) : Array.isArray(selector) ? selector : [selector]; + elements.forEach(function (element) { + if (typeof element[_this3.constructor.name] === 'undefined') { + var instance = new bulmaSlider(element, options); + element[_this3.constructor.name] = instance; + instances.push(instance); + } else { + instances.push(element[_this3.constructor.name]); + } + }); + + return instances; + } + }]); + + return bulmaSlider; +}(__WEBPACK_IMPORTED_MODULE_0__events__["a" /* default */]); + +/* harmony default export */ __webpack_exports__["default"] = (bulmaSlider); + +/***/ }), +/* 1 */ +/***/ (function(module, __webpack_exports__, __webpack_require__) { + +"use strict"; +var _createClass = function () { function defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }(); + +function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } } + +var EventEmitter = function () { + function EventEmitter() { + var listeners = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : []; + + _classCallCheck(this, EventEmitter); + + this._listeners = new Map(listeners); + this._middlewares = new Map(); + } + + _createClass(EventEmitter, [{ + key: "listenerCount", + value: function listenerCount(eventName) { + if (!this._listeners.has(eventName)) { + return 0; + } + + var eventListeners = this._listeners.get(eventName); + return eventListeners.length; + } + }, { + key: "removeListeners", + value: function removeListeners() { + var _this = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + var middleware = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : false; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this.removeListeners(e, middleware); + }); + } else { + this._listeners.delete(eventName); + + if (middleware) { + this.removeMiddleware(eventName); + } + } + } else { + this._listeners = new Map(); + } + } + }, { + key: "middleware", + value: function middleware(eventName, fn) { + var _this2 = this; + + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this2.middleware(e, fn); + }); + } else { + if (!Array.isArray(this._middlewares.get(eventName))) { + this._middlewares.set(eventName, []); + } + + this._middlewares.get(eventName).push(fn); + } + } + }, { + key: "removeMiddleware", + value: function removeMiddleware() { + var _this3 = this; + + var eventName = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : null; + + if (eventName !== null) { + if (Array.isArray(eventName)) { + name.forEach(function (e) { + return _this3.removeMiddleware(e); + }); + } else { + this._middlewares.delete(eventName); + } + } else { + this._middlewares = new Map(); + } + } + }, { + key: "on", + value: function on(name, callback) { + var _this4 = this; + + var once = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + if (Array.isArray(name)) { + name.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + name = name.toString(); + var split = name.split(/,|, | /); + + if (split.length > 1) { + split.forEach(function (e) { + return _this4.on(e, callback); + }); + } else { + if (!Array.isArray(this._listeners.get(name))) { + this._listeners.set(name, []); + } + + this._listeners.get(name).push({ once: once, callback: callback }); + } + } + } + }, { + key: "once", + value: function once(name, callback) { + this.on(name, callback, true); + } + }, { + key: "emit", + value: function emit(name, data) { + var _this5 = this; + + var silent = arguments.length > 2 && arguments[2] !== undefined ? arguments[2] : false; + + name = name.toString(); + var listeners = this._listeners.get(name); + var middlewares = null; + var doneCount = 0; + var execute = silent; + + if (Array.isArray(listeners)) { + listeners.forEach(function (listener, index) { + // Start Middleware checks unless we're doing a silent emit + if (!silent) { + middlewares = _this5._middlewares.get(name); 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All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Submit COMPASS no-regression benchmark jobs to OSMO. + +Fires one ``rl_es_eval_workflow.yaml`` submission per ``--environments`` entry, +all using the same ``--embodiment``. Re-run with different ``--embodiment`` +values to cover the full embodiment x environment matrix. + +Results land in W&B at ``/bm___``. +Each run logs ``eval/goal_reached_rate``, ``eval/fall_down_rate``, +``eval/total_travel_time``, and ``eval/weighted_travel_time`` -- pull those out +of W&B for the regression assessment. + +Usage: + export COMPASS_OSMO_REGISTRY=nvcr.io// + export WANDB_API_KEY= + export HF_TOKEN= + + # Default 5-scene sweep for one embodiment. + python osmo/run_benchmark.py \\ + --experiment-name release_1.6 \\ + --wandb-project-name compass_release_1.6_benchmark \\ + --checkpoint-artifact \\ + --embodiment g1 + + # Single-cell smoke (skip build/push by reusing a pre-built image). + python osmo/run_benchmark.py \\ + --experiment-name release_1.6_smoke \\ + --wandb-project-name compass_release_1.6_benchmark \\ + --checkpoint-artifact \\ + --embodiment g1 --environments combined_single_rack \\ + --image-name nvcr.io///compass_release_1.6: +""" + +import argparse +import getpass +import os +import shlex +import subprocess +import sys +import uuid +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parent.parent +WORKFLOW_PATH = Path(__file__).resolve().parent / "workflows" / "rl_es_eval_workflow.yaml" +DOCKERFILE = "docker/Dockerfile.rl" + +DEFAULT_ENVIRONMENTS = [ + "simple_office", + "warehouse_single_rack", + "warehouse_multi_rack", + "combined_single_rack", + "combined_multi_rack", +] + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Submit a COMPASS no-regression benchmark sweep to OSMO. " + "One rl_es_eval_workflow.yaml job is fired per --environments entry.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("--image-name", + "-m", + default="", + help="Pre-built docker image. If empty, build+push using " + "--registry-prefix.") + parser.add_argument("--registry-prefix", + default=os.environ.get("COMPASS_OSMO_REGISTRY", ""), + help="Registry prefix for build+push (e.g. nvcr.io//); " + "defaults to $COMPASS_OSMO_REGISTRY.") + parser.add_argument("--experiment-name", + "-e", + type=str, + required=True, + help="Short identifier; used in workflow_name, wandb run name, image tag.") + parser.add_argument("--checkpoint-artifact", + "-p", + type=str, + required=True, + help="wandb artifact for the residual checkpoint to benchmark.") + parser.add_argument("--distillation-ckpt-artifact", + "-d", + type=str, + default=None, + help="Optional wandb artifact for a distillation checkpoint.") + parser.add_argument("--wandb-project-name", + "-n", + type=str, + required=True, + help="wandb project to log into.") + parser.add_argument("--embodiment", + type=str, + default="h1", + help="Embodiment to benchmark; re-run with different values for " + "full matrix.") + parser.add_argument("--environments", + nargs="+", + type=str, + default=DEFAULT_ENVIRONMENTS, + help="Scenes to iterate over; one OSMO eval job per entry.") + parser.add_argument("--pool", + default="", + help="OSMO pool to target (e.g. isaac-dev-l40-03); " + "if empty, uses the profile's default pool.") + parser.add_argument("--dry-run", + action="store_true", + help="Print the osmo workflow submit commands without executing them.") + parser.add_argument("--prompt", + action="store_true", + help="Interactively prompt for missing credentials.") + return parser.parse_args() + + +def get_credential(name: str, prompt: bool) -> str: + val = os.environ.get(name, "") + if val: + return val + if prompt: + return getpass.getpass(f"Enter {name}: ") + sys.stderr.write(f"ERROR: ${name} is not set. Either export it or pass --prompt.\n") + sys.exit(2) + + +def build_and_push_image(experiment: str, registry_prefix: str, dry_run: bool) -> str: + if not registry_prefix: + sys.stderr.write("ERROR: --image-name not given and --registry-prefix is empty " + "(also $COMPASS_OSMO_REGISTRY).\n") + sys.exit(2) + image = f"{registry_prefix.rstrip('/')}/compass_{experiment}:{uuid.uuid4().hex[:8]}" + cmds = [ + ["docker", "build", "--network=host", "-t", image, "-f", DOCKERFILE, + str(REPO_ROOT)], + ["docker", "push", image], + ] + for cmd in cmds: + print(f"+ {' '.join(shlex.quote(c) for c in cmd)}") + if not dry_run: + subprocess.check_call(cmd) + return image + + +def submit_workflow(set_args: dict, pool: str, dry_run: bool) -> None: + cmd = ["osmo", "workflow", "submit", str(WORKFLOW_PATH)] + if pool: + cmd += ["--pool", pool] + cmd += ["--set"] + [f"{k}={v}" for k, v in set_args.items()] + print(f"+ {' '.join(shlex.quote(c) for c in cmd)}") + if not dry_run: + subprocess.check_call(cmd) + + +def main() -> None: + args = parse_args() + + wandb_key = get_credential("WANDB_API_KEY", args.prompt) + hf_token = get_credential("HF_TOKEN", args.prompt) + + if args.image_name: + image = args.image_name + else: + image = build_and_push_image(args.experiment_name, args.registry_prefix, args.dry_run) + + for environment in args.environments: + run_name = f"bm_{args.embodiment}_{environment}_{args.experiment_name}" + set_args = { + "workflow_name": run_name, + "image": image, + "wandb_api_key": wandb_key, + "wandb_project_name": args.wandb_project_name, + "wandb_run_name": run_name, + "hf_token": hf_token, + "checkpoint_artifact": args.checkpoint_artifact, + "embodiment": args.embodiment, + "environment": environment, + } + if args.distillation_ckpt_artifact: + set_args["distillation_ckpt_artifact"] = args.distillation_ckpt_artifact + submit_workflow(set_args, args.pool, args.dry_run) + + +if __name__ == "__main__": + main() diff --git a/osmo/run_osmo.py b/osmo/run_osmo.py new file mode 100755 index 0000000..70997ea --- /dev/null +++ b/osmo/run_osmo.py @@ -0,0 +1,284 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Submit COMPASS training/eval/record/distill workflows to OSMO. + +Replaces the interactive shell launcher used in the internal repo with a +non-interactive Python CLI. Reads credentials from environment variables +(``WANDB_API_KEY``, ``HF_TOKEN``) with an opt-in ``--prompt`` fallback. + +Usage: + # Pre-built image, env vars already exported. The X-Mobility base + # checkpoint and the COMPASS USDs are downloaded inside the workflow + # from huggingface.co/nvidia/X-Mobility and huggingface.co/nvidia/COMPASS + # respectively, so no --base-policy-ckpt flag is needed. + python osmo/run_osmo.py train \\ + --experiment-name pilot \\ + --wandb-project compass-rl \\ + --image nvcr.io//compass_pilot: + + # Build+push the image automatically + export COMPASS_OSMO_REGISTRY=nvcr.io// + python osmo/run_osmo.py train --experiment-name pilot --wandb-project compass-rl + + # Inspect the would-be submit command without executing it + python osmo/run_osmo.py train --experiment-name pilot --wandb-project compass-rl \\ + --image nvcr.io//img:tag --dry-run + +See https://nvlabs.github.io/COMPASS/docs/osmo.html for the full prerequisites +and per-subcommand examples. +""" + +import argparse +import getpass +import os +import shlex +import subprocess +import sys +import uuid +from pathlib import Path + +REPO_ROOT = Path(__file__).resolve().parent.parent +WORKFLOWS_DIR = Path(__file__).resolve().parent / "workflows" + +# subcommand -> (workflow YAML filename, Dockerfile path relative to REPO_ROOT) +SUBCOMMAND_CONFIG = { + "train": ("rl_es_train_workflow.yaml", "docker/Dockerfile.rl"), + "eval": ("rl_es_eval_workflow.yaml", "docker/Dockerfile.rl"), + "record": ("rl_es_record_workflow.yaml", "docker/Dockerfile.rl"), + "distill": ("distillation_train_workflow.yaml", "docker/Dockerfile.distillation"), +} + + +def parse_args(): + parser = argparse.ArgumentParser( + description="Submit a COMPASS workflow to OSMO.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + sub = parser.add_subparsers(dest="subcommand", required=True) + + def add_common(sp): + sp.add_argument("--experiment-name", + required=True, + help="Short identifier; used in workflow_name and image tag.") + sp.add_argument( + "--image", + default="", + help="Pre-built docker image. If empty, build+push using --registry-prefix.") + sp.add_argument("--registry-prefix", + default=os.environ.get("COMPASS_OSMO_REGISTRY", ""), + help="Registry prefix for build+push (e.g. nvcr.io//); " + "defaults to $COMPASS_OSMO_REGISTRY.") + sp.add_argument("--dry-run", + action="store_true", + help="Print the osmo workflow submit command without executing it.") + sp.add_argument("--prompt", + action="store_true", + help="Interactively prompt for missing credentials.") + + train = sub.add_parser("train", help="Submit residual RL training.") + add_common(train) + train.add_argument("--wandb-project", + required=True, + help="wandb project to log into (e.g. compass-rl).") + train.add_argument("--resume-ckpt", + default="", + help="wandb artifact for resuming from a prior residual checkpoint.") + train.add_argument("--no-residual", + action="store_true", + help="Skip the residual head (use base policy only).") + train.add_argument("--embodiment", default="", help="Override the gin-config embodiment.") + train.add_argument("--environment", default="", help="Override the gin-config environment.") + train.add_argument("--num-gpus", + type=int, + default=8, + help="Number of GPUs (= torchrun ranks). All counts use the same " + "distributed workflow YAML; the trainer's distributed code paths are " + "world_size-aware so num_gpus=1 also works as a single-rank run.") + + evl = sub.add_parser("eval", help="Submit residual RL evaluation.") + add_common(evl) + evl.add_argument("--wandb-project", + required=True, + help="wandb project to log into (e.g. compass-rl).") + evl.add_argument("--checkpoint", + required=True, + help="wandb artifact for the residual checkpoint to evaluate.") + evl.add_argument("--distillation-ckpt", + default="", + help="wandb artifact for an optional distillation checkpoint.") + evl.add_argument("--no-residual", + action="store_true", + help="Evaluate without the residual head (base policy only).") + evl.add_argument("--embodiment", default="", help="Override the gin-config embodiment.") + evl.add_argument("--environment", default="", help="Override the gin-config environment.") + + rec = sub.add_parser("record", help="Submit distillation-data recording.") + add_common(rec) + rec.add_argument("--dataset-name", + required=True, + help="OSMO output dataset name for recorded specialist data.") + + dis = sub.add_parser("distill", help="Submit generalist distillation training.") + add_common(dis) + dis.add_argument("--wandb-project", + required=True, + help="wandb project to log into (e.g. compass-distill).") + dis.add_argument("--dataset-name", + required=True, + help="OSMO input dataset name (specialist rollouts).") + dis.add_argument("--checkpoint", + default="", + help="wandb artifact for an optional resume checkpoint.") + dis.add_argument("--train-config", + default="distillation_config", + help="Gin config name without .gin (default: distillation_config).") + + return parser.parse_args() + + +def get_credential(name: str, prompt: bool) -> str: + val = os.environ.get(name, "") + if val: + return val + if prompt: + return getpass.getpass(f"Enter {name}: ") + sys.stderr.write(f"ERROR: ${name} is not set. Either export it or pass --prompt.\n") + sys.exit(2) + + +def build_and_push_image(experiment: str, registry_prefix: str, dockerfile: str, + dry_run: bool) -> str: + if not registry_prefix: + sys.stderr.write("ERROR: --image not given and --registry-prefix is empty " + "(also $COMPASS_OSMO_REGISTRY).\n") + sys.exit(2) + image = f"{registry_prefix.rstrip('/')}/compass_{experiment}:{uuid.uuid4().hex[:8]}" + cmds = [ + ["docker", "build", "--network=host", "-t", image, "-f", dockerfile, + str(REPO_ROOT)], + ["docker", "push", image], + ] + for cmd in cmds: + print(f"+ {' '.join(shlex.quote(c) for c in cmd)}") + if not dry_run: + subprocess.check_call(cmd) + return image + + +def submit_workflow(yaml_path: Path, set_args: dict, dry_run: bool) -> None: + cmd = ["osmo", "workflow", "submit", str(yaml_path), "--set"] + cmd += [f"{k}={v}" for k, v in set_args.items()] + print(f"+ {' '.join(shlex.quote(c) for c in cmd)}") + if not dry_run: + subprocess.check_call(cmd) + + +def cmd_train(args, image: str, wandb_key: str, hf_token: str) -> None: + if args.num_gpus < 1: + sys.stderr.write(f"ERROR: --num-gpus must be >= 1 (got {args.num_gpus}).\n") + sys.exit(2) + yaml_path = WORKFLOWS_DIR / SUBCOMMAND_CONFIG["train"][0] + set_args = { + "workflow_name": f"compass_rl_es_{args.experiment_name}", + "image": image, + "num_gpus": args.num_gpus, + "wandb_api_key": wandb_key, + "wandb_project_name": args.wandb_project, + "wandb_run_name": args.experiment_name, + "hf_token": hf_token, + "resume_ckpt_artifact": args.resume_ckpt, + "no_residual": "1" if args.no_residual else "", + "embodiment": args.embodiment, + "environment": args.environment, + } + submit_workflow(yaml_path, set_args, args.dry_run) + + +def cmd_eval(args, image: str, wandb_key: str, hf_token: str) -> None: + yaml_path = WORKFLOWS_DIR / SUBCOMMAND_CONFIG["eval"][0] + set_args = { + "workflow_name": f"compass_rl_es_{args.experiment_name}", + "image": image, + "wandb_api_key": wandb_key, + "wandb_project_name": args.wandb_project, + "wandb_run_name": args.experiment_name, + "hf_token": hf_token, + "checkpoint_artifact": args.checkpoint, + "distillation_ckpt_artifact": args.distillation_ckpt, + "no_residual": "1" if args.no_residual else "", + "embodiment": args.embodiment, + "environment": args.environment, + } + submit_workflow(yaml_path, set_args, args.dry_run) + + +def cmd_record(args, image: str, wandb_key: str, hf_token: str) -> None: + yaml_path = WORKFLOWS_DIR / SUBCOMMAND_CONFIG["record"][0] + set_args = { + "workflow_name": f"compass_rl_es_{args.experiment_name}", + "image": image, + "wandb_api_key": wandb_key, + "hf_token": hf_token, + "dataset_name": args.dataset_name, + } + submit_workflow(yaml_path, set_args, args.dry_run) + + +def cmd_distill(args, image: str, wandb_key: str, hf_token: str) -> None: + del hf_token # distillation workflow doesn't need HF auth + yaml_path = WORKFLOWS_DIR / SUBCOMMAND_CONFIG["distill"][0] + set_args = { + "workflow_name": f"compass_distillation_{args.experiment_name}", + "image": image, + "wandb_api_key": wandb_key, + "wandb_project_name": args.wandb_project, + "wandb_run_name": args.experiment_name, + "dataset_name": args.dataset_name, + "checkpoint_artifact": args.checkpoint, + "train_config": args.train_config, + } + submit_workflow(yaml_path, set_args, args.dry_run) + + +SUBCOMMAND_DISPATCH = { + "train": cmd_train, + "eval": cmd_eval, + "record": cmd_record, + "distill": cmd_distill, +} + + +def main() -> None: + args = parse_args() + + wandb_key = get_credential("WANDB_API_KEY", args.prompt) + # HF_TOKEN is consumed by RL workflows; distillation doesn't need it. + hf_token = "" + if args.subcommand in ("train", "eval", "record"): + hf_token = get_credential("HF_TOKEN", args.prompt) + + if args.image: + image = args.image + else: + dockerfile = SUBCOMMAND_CONFIG[args.subcommand][1] + image = build_and_push_image(args.experiment_name, args.registry_prefix, dockerfile, + args.dry_run) + + SUBCOMMAND_DISPATCH[args.subcommand](args, image, wandb_key, hf_token) + + +if __name__ == "__main__": + main() diff --git a/osmo/workflows/distillation_train_workflow.yaml b/osmo/workflows/distillation_train_workflow.yaml new file mode 100644 index 0000000..f06df14 --- /dev/null +++ b/osmo/workflows/distillation_train_workflow.yaml @@ -0,0 +1,71 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +version: 2 +workflow: + name: {{workflow_name}} + resources: + default: + cpu: 80 + gpu: 4 + memory: 800Gi + storage: 800Gi + platform: dgx-h100 + tasks: + - name: train + image: {{image}} + command: ["/bin/bash"] + args: ["/tmp/entry.sh"] + files: + - path: /tmp/entry.sh + contents: | + set -ex + + # Generate your W&B API key: https://wandb.ai/authorize + export WANDB_API_KEY={{wandb_api_key}} + + # Create directory to store the trainning outputs. + OUTPUT_DIR="{{output}}/outputs" + mkdir -p $OUTPUT_DIR + mkdir -p $OUTPUT_DIR/tensorboard_logs/lightning_logs + + # Get dataset folder. + DATASET_FOLDER_NAME=$(echo {{dataset_name}} | cut -d'/' -f2 | cut -d':' -f1) + + TRAIN_CMD="python3 distillation_train.py -c configs/{{train_config}}.gin -o $OUTPUT_DIR -d {{input:0}}/$DATASET_FOLDER_NAME/data -n {{wandb_project_name}} -r {{wandb_run_name}} --logger wandb" + + # Get pretrained checkpoint if provided. + if [ -z "{{checkpoint_artifact}}" ]; then + echo "No pretrained checkpoint, train from scratch." + else + echo "Downloading checkpoint: {{checkpoint_artifact}}" + wandb artifact get {{checkpoint_artifact}} --root ./ + TRAIN_CMD="$TRAIN_CMD -p ./model.ckpt" + fi + + eval $TRAIN_CMD + + inputs: + - dataset: + name: {{dataset_name}} + outputs: + - dataset: + name: ml_nav_outputs:{{workflow_id}} + path: outputs + +default-values: + image: "" + workflow_name: compass_distillation + train_config: distillation_config + checkpoint_artifact: "" diff --git a/osmo/workflows/rl_es_eval_workflow.yaml b/osmo/workflows/rl_es_eval_workflow.yaml new file mode 100644 index 0000000..8d69ec0 --- /dev/null +++ b/osmo/workflows/rl_es_eval_workflow.yaml @@ -0,0 +1,119 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +version: 2 +workflow: + name: {{workflow_name}} + resources: + default: + cpu: 20 + gpu: 2 + memory: 200Gi + platform: ovx-l40 + storage: 400Gi + tasks: + - name: train + image: {{image}} + command: ["/bin/bash"] + args: ["/tmp/entry.sh"] + environment: + ACCEPT_EULA: Y + files: + - contents: | + # Set up environment + apt update + + set -e + # Hide conflicting Vulkan files, if needed + if [ -e "/usr/share/vulkan" ] && [ -e "/etc/vulkan" ]; then + mv /usr/share/vulkan /usr/share/vulkan_hidden + fi + + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli login --token {{hf_token}} + + # Generate your W&B API key: https://wandb.ai/authorize + export WANDB_API_KEY={{wandb_api_key}} + + # Download and unpack the COMPASS USDs from HuggingFace. + echo "Downloading compass_usds.zip from huggingface.co/nvidia/COMPASS" + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli \ + download nvidia/COMPASS compass_usds.zip --local-dir ./ + unzip -q -o compass_usds.zip + mv groot_mobility_rl_es_usds/usd ./compass/rl_env/exts/mobility_es/mobility_es/ + rm -rf groot_mobility_rl_es_usds compass_usds.zip + + # Create directory to store the training outputs. + OUTPUT_DIR="{{output}}/outputs" + mkdir -p $OUTPUT_DIR + + # Download X-Mobility base-policy checkpoint from HuggingFace. + echo "Downloading X-Mobility checkpoint from huggingface.co/nvidia/X-Mobility" + mkdir -p ./base_policy + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli \ + download nvidia/X-Mobility x_mobility-nav2-semantic_action_path.ckpt --local-dir ./base_policy + mv ./base_policy/x_mobility-nav2-semantic_action_path.ckpt ./base_policy/model.ckpt + + # Download residual policy. + echo "Downloading residual policy checkpoint: {{checkpoint_artifact}}" + ${ISAACLAB_PATH}/isaaclab.sh -p -m wandb artifact get {{checkpoint_artifact}} --root ./residual_policy/ + EVAL_CKPT=$(ls ./residual_policy/model_*.pt | sort -V | tail -n 1) + + # Download distillation policy if given. + DESTILLATION_CKPT_ARTIFACT={{distillation_ckpt_artifact}} + if [[ -n $DESTILLATION_CKPT_ARTIFACT ]]; then + ${ISAACLAB_PATH}/isaaclab.sh -p -m wandb artifact get $DESTILLATION_CKPT_ARTIFACT --root ./destillation_policy/ + DESTILLATION_CKPT=./destillation_policy/model.ckpt + fi + + # Launch evaluation. + CMD="${ISAACLAB_PATH}/isaaclab.sh -p run.py -c configs/eval_config.gin --enable_cameras -o $OUTPUT_DIR -p $EVAL_CKPT -n {{wandb_project_name}} -r {{wandb_run_name}} --logger wandb --headless --video --video_interval 1" + + EMBODIMENT={{embodiment}} + if [[ -n $EMBODIMENT ]]; then + CMD+=" --embodiment $EMBODIMENT" + fi + + ENVIRONMENT={{environment}} + if [[ -n $ENVIRONMENT ]]; then + CMD+=" --environment $ENVIRONMENT" + fi + + if [[ -n $DESTILLATION_CKPT_ARTIFACT ]]; then + CMD+=" -d $DESTILLATION_CKPT" + fi + + NO_RESIDUAL={{no_residual}} + if [[ -z $NO_RESIDUAL ]]; then + CMD+=" -b ./base_policy/model.ckpt" + fi + + echo $CMD + eval $CMD + + path: /tmp/entry.sh + - contents: | + { + "launch_config": { + "headless": true + } + } + path: /tmp/config.json + +default-values: + image: "" + workflow_name: compass_rl_es + no_residual: "" + distillation_ckpt_artifact: "" + embodiment: "" + environment: "" diff --git a/osmo/workflows/rl_es_record_workflow.yaml b/osmo/workflows/rl_es_record_workflow.yaml new file mode 100644 index 0000000..605fd05 --- /dev/null +++ b/osmo/workflows/rl_es_record_workflow.yaml @@ -0,0 +1,91 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +version: 2 +workflow: + name: {{workflow_name}} + resources: + default: + cpu: 20 + gpu: 2 + memory: 200Gi + platform: ovx-l40 + storage: 1200Gi + tasks: + - name: train + image: {{image}} + command: ["/bin/bash"] + args: ["/tmp/entry.sh"] + environment: + ACCEPT_EULA: Y + files: + - contents: | + # Set up environment + apt update + + set -e + # Hide conflicting Vulkan files, if needed + if [ -e "/usr/share/vulkan" ] && [ -e "/etc/vulkan" ]; then + mv /usr/share/vulkan /usr/share/vulkan_hidden + fi + + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli login --token {{hf_token}} + + # Generate your W&B API key: https://wandb.ai/authorize + export WANDB_API_KEY={{wandb_api_key}} + + # Download and unpack the COMPASS USDs from HuggingFace. + echo "Downloading compass_usds.zip from huggingface.co/nvidia/COMPASS" + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli \ + download nvidia/COMPASS compass_usds.zip --local-dir ./ + unzip -q -o compass_usds.zip + mv groot_mobility_rl_es_usds/usd ./compass/rl_env/exts/mobility_es/mobility_es/ + rm -rf groot_mobility_rl_es_usds compass_usds.zip + + # Create directory to store the training outputs. + OUTPUT_DIR="{{output}}/outputs" + mkdir -p $OUTPUT_DIR + + # Download X-Mobility base-policy checkpoint from HuggingFace. + echo "Downloading X-Mobility checkpoint from huggingface.co/nvidia/X-Mobility" + mkdir -p ./base_policy + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli \ + download nvidia/X-Mobility x_mobility-nav2-semantic_action_path.ckpt --local-dir ./base_policy + mv ./base_policy/x_mobility-nav2-semantic_action_path.ckpt ./base_policy/model.ckpt + + # Launch recording. + CMD="${ISAACLAB_PATH}/isaaclab.sh -p record.py -c configs/distillation_dataset_config.yaml -o $OUTPUT_DIR -b ./base_policy/model.ckpt --dataset-name {{dataset_name}}" + + echo $CMD + eval $CMD + + path: /tmp/entry.sh + - contents: | + { + "launch_config": { + "headless": true + } + } + path: /tmp/config.json + + outputs: + - dataset: + name: {{dataset_name}}:{{workflow_id}} + path: outputs/{{dataset_name}}/data + + +default-values: + image: "" + workflow_name: compass_rl_es + dataset_name: compass_rl_es_experts diff --git a/osmo/workflows/rl_es_train_workflow.yaml b/osmo/workflows/rl_es_train_workflow.yaml new file mode 100644 index 0000000..4ffc1f2 --- /dev/null +++ b/osmo/workflows/rl_es_train_workflow.yaml @@ -0,0 +1,153 @@ +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# N-GPU distributed residual-RL training. Launches the train phase via +# torch.distributed.run --nproc_per_node={{num_gpus}} so each rank owns its +# own Isaac Sim instance pinned to a different GPU. Per-rank rollouts are +# collected in parallel ({{num_gpus}} x num_envs total transitions per iter); +# gradients are synced via manual all-reduce in PPO.update. The eval phase at +# the end is left as a single-process invocation since eval doesn't benefit +# from multi-GPU and the residual policy is small. +# +# cpu / memory are sized for an 8x L40 node and are NOT scaled per +# num_gpus — running with num_gpus < 8 over-allocates harmlessly; running +# with num_gpus > 8 would require bumping both knobs and picking a larger +# platform. +version: 2 +workflow: + name: {{workflow_name}} + resources: + default: + cpu: 80 + gpu: {{num_gpus}} + memory: 800Gi + platform: ovx-l40 + storage: 400Gi + tasks: + - name: train + image: {{image}} + command: ["/bin/bash"] + args: ["/tmp/entry.sh"] + environment: + ACCEPT_EULA: Y + files: + - contents: | + # Set up environment + apt update + + set -e + # Hide conflicting Vulkan files, if needed + if [ -e "/usr/share/vulkan" ] && [ -e "/etc/vulkan" ]; then + mv /usr/share/vulkan /usr/share/vulkan_hidden + fi + + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli login --token {{hf_token}} + + # Generate your W&B API key: https://wandb.ai/authorize + export WANDB_API_KEY={{wandb_api_key}} + + # Download and unpack the COMPASS USDs from HuggingFace. + echo "Downloading compass_usds.zip from huggingface.co/nvidia/COMPASS" + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli \ + download nvidia/COMPASS compass_usds.zip --local-dir ./ + unzip -q -o compass_usds.zip + mv groot_mobility_rl_es_usds/usd ./compass/rl_env/exts/mobility_es/mobility_es/ + rm -rf groot_mobility_rl_es_usds compass_usds.zip + + # Create directory to store the training outputs. + OUTPUT_DIR="{{output}}/outputs" + mkdir -p $OUTPUT_DIR + + # Download X-Mobility base-policy checkpoint from HuggingFace. + echo "Downloading X-Mobility checkpoint from huggingface.co/nvidia/X-Mobility" + ${ISAACLAB_PATH}/isaaclab.sh -p -m huggingface_hub.commands.huggingface_cli \ + download nvidia/X-Mobility x_mobility-nav2-semantic_action_path.ckpt --local-dir ./ + mv x_mobility-nav2-semantic_action_path.ckpt model.ckpt + + # Download resume checkpoint if provided. + RESUME_CKPT_ARTIFACT={{resume_ckpt_artifact}} + if [[ -n $RESUME_CKPT_ARTIFACT ]]; then + echo "Downloading resume checkpoint: $RESUME_CKPT_ARTIFACT" + ${ISAACLAB_PATH}/isaaclab.sh -p -m wandb artifact get $RESUME_CKPT_ARTIFACT --root ./resume_ckpt/ + RESUME_CKPT=$(ls ./resume_ckpt/model_*.pt | sort -V | tail -n 1) + fi + + # Launch training across {{num_gpus}} GPUs via torchrun. Each rank + # spawns its own Isaac Sim instance + env; gradients sync via manual + # all-reduce in PPO.update. Total parallel envs = {{num_gpus}} * + # num_envs. We pin --num_envs 32 here because the gin default of 64 + # OOMs on L40 (48 GB) at this batch size; 32 fits comfortably. + TRAIN_CMD="${ISAACLAB_PATH}/isaaclab.sh -p -m torch.distributed.run --nproc_per_node={{num_gpus}} run.py --distributed -c configs/train_config.gin --enable_cameras -o $OUTPUT_DIR -n {{wandb_project_name}} -r {{wandb_run_name}} --logger wandb --headless --video --num_envs 32" + + EMBODIMENT={{embodiment}} + if [[ -n $EMBODIMENT ]]; then + TRAIN_CMD+=" --embodiment $EMBODIMENT" + fi + + ENVIRONMENT={{environment}} + if [[ -n $ENVIRONMENT ]]; then + TRAIN_CMD+=" --environment $ENVIRONMENT" + fi + + NO_RESIDUAL={{no_residual}} + if [[ -z $NO_RESIDUAL ]]; then + TRAIN_CMD+=" -b ./model.ckpt" + fi + + if [[ -n $RESUME_CKPT_ARTIFACT ]]; then + TRAIN_CMD+=" -p $RESUME_CKPT" + fi + + echo $TRAIN_CMD + eval $TRAIN_CMD + + # Launch evaluation of last checkpoint. Eval is single-process — it + # uses one of the allocated GPUs and leaves the rest idle, which is + # fine for a one-shot eval at the end of training. + eval_ckpt=$(ls $OUTPUT_DIR/model_*.pt | sort -V | tail -n 1) + ckpt_name=$(basename "$eval_ckpt") + EVAL_CMD="${ISAACLAB_PATH}/isaaclab.sh -p run.py -c configs/eval_config.gin --enable_cameras -o $OUTPUT_DIR -p $eval_ckpt -n {{wandb_project_name}} -r eval_{{wandb_run_name}}_$ckpt_name --logger wandb --headless --video --video_interval 1" + if [[ -z $NO_RESIDUAL ]]; then + EVAL_CMD+=" -b ./model.ckpt" + fi + + if [[ -n $EMBODIMENT ]]; then + EVAL_CMD+=" --embodiment $EMBODIMENT" + fi + + if [[ -n $ENVIRONMENT ]]; then + EVAL_CMD+=" --environment $ENVIRONMENT" + fi + + echo $EVAL_CMD + eval $EVAL_CMD + + path: /tmp/entry.sh + - contents: | + { + "launch_config": { + "headless": true + } + } + path: /tmp/config.json + +default-values: + image: "" + workflow_name: compass_rl_es_train + num_gpus: 8 + no_residual: "" + resume_ckpt_artifact: "" + embodiment: "" + environment: "" diff --git a/record.py b/record.py index 41f475e..73c428e 100644 --- a/record.py +++ b/record.py @@ -132,7 +132,7 @@ def main(): parser.add_argument('--wandb-project-name', '-n', type=str, - default='afm_rl_enhance_record', + required=True, help='The project name of W&B.') parser.add_argument('--wandb-entity-name', '-e', diff --git a/release_tracker.md b/release_tracker.md new file mode 100644 index 0000000..6196a06 --- /dev/null +++ b/release_tracker.md @@ -0,0 +1,367 @@ +# COMPASS 1.6 Release Tracker + +> Working tracker for the next release. Distilled into `CHANGELOG.md` at ship time; do not duplicate that history here. + +**Target version:** `1.6.0` +**Target date:** TBD +**Release manager:** @liuw +**Status legend:** ⬜ not started · 🟡 in progress · 🟢 done · 🔴 blocked · ⚪ deferred + +**Integration branch:** `liuw/benchmark_port` (off `liuw/training_time_improve`) — 13 commits ahead of `main` (`30612e4`, 2025-10-16). Nothing merged yet. Release strategy: **single squash-merge to `main`** (no per-workstream PR stack). + +## Summary + +| # | Workstream | Priority | Status | Owner | +|---|-----------|----------|--------|-------| +| 1 | OSMO code migration (training runnable on OSMO) | P0 | 🟡 | @liuw | +| 2 & 3 | Isaac Lab 2.1 → 3.0+ upgrade (Bucket A) **+** NuRec PR-2 deferred to post-1.6 | P0 (Bucket A) / ⚪ (NuRec PR-2) | 🟢 (A) · ⚪ (PR-2) | @samc + @liuw | +| 4 | Agentic skills for automatic model training (also enables SAGE) | P1 | 🟡 | @liuw | +| 5 | Auto OMap generation from USDs | P1 | 🟡 | @liuw | +| 6 | GitHub Pages docs site (X-Mobility → COMPASS) | P1 | 🟡 | @liuw | +| 7 | Docker-as-venv dev environment (`docker/run.sh` + `docker/activate`) | P1 | 🟡 | @liuw | +| 8 | Pre-release leak audit + sanitization | P0 | 🟡 | @liuw | +| 9 | CI/CD setup + dependency pinning | P1 | 🟡 | @liuw | +| 10 | Agentic skills refresh + new onboarding skills | P1 | 🟡 | @liuw | +| 11 | Multi-GPU PPO training + perf instrumentation | TBD | 🟡 | @liuw | +| — | No-regression benchmark (gate) | P0 | ⬜ | TBD | +| — | CHANGELOG + version bump + tag | P0 | ⬜ | @liuw | + +## Branch state (current snapshot) + +All 12 commits below sit on `liuw/training_time_improve` (oldest → newest); nothing has merged to `main`. The per-workstream branches in `git branch -vv` (`liuw/osmo_migration`, `liuw/agentic_skills_migration`, …, `liuw/skills_enhancement`) are stacking checkpoints, not separate trees. + +| Commit | Workstream | Note | +|--------|-----------|------| +| `22b25ef` | #2&3 PR-1 | Isaac Lab 3.0 API migration (Bucket A) | +| `1253f2a` | #1 | OSMO workflows + `run_osmo.py` (initial port) | +| `000c3be` | #4 | Migrate `compass` Claude Code skill from internal repo | +| `b20fb67` | #5 | USD-derived OMap generator + loader fallback | +| `725b79c` | #7 | Docker-as-venv (`docker/run.sh` + `docker/activate`) | +| `f4d89b8` | #6 | Docs site (academic landing + Sphinx handbook) | +| `d0275cc` | #8 | OSMO sanitization (HF asset sources) | +| `3cc5eaf` | #9 | Pre-commit CI workflow + requirements pinning | +| `e0b6f20` | #10 | `/compass` refresh + deploy/debug/newembodiment skills | +| `d605901` | #1 | Thread `--embodiment` / `--environment` through OSMO train | +| `424a96a` | gate | Add benchmark.py sanitization subtask under gate | +| `9f478af` | #11 | Multi-GPU PPO training + perf instrumentation | + +**Still missing in code for 1.6:** CHANGELOG `[1.6.0]` entry, version bump, pre-commit `lib2to3` toolchain fix. NuRec PR-2 (Buckets B+E+H) is **deferred to post-1.6** — Bucket A (`22b25ef`) is the only NuRec-related code in 1.6. + +## Dependencies + +``` +#2&3 cherry-pick from samc/support_nurec_assets_isaaclab_3.0: + PR-1 (Isaac Lab 3.0 migration) ──► PR-2 (NuRec assets + occupancy_map) ──┐ + │ +OSMO (#1) ──────────────────────────────────────────────────────────────────┤ +Agentic skills (#4) ────────────────────────────────────────────────────────┼──► Benchmark ──► Release +Auto-OMap (#5) ─────────────────────────────────────────────────────────────┘ +Docs (#6), Dev environment (#7) — parallel; finalized before tag +Agentic skills refresh (#10) ── depends on Dev environment (#7) and Sanitization (#8) +PR-FOLLOWUP (multi-cam recorder + video upload + debug images) — post-release, outside critical path +``` + +**SAGE note:** SAGE training is enabled by the agentic skills (#4). The only SAGE-specific gap is auto OMap generation (#5), which makes SAGE-driven training smoother. No standalone "SAGE integration" workstream. + +--- + +## 1. OSMO code migration — P0 + +Bring OSMO training-launch code from internal `gitlab-master.nvidia.com/ml_nav/compass` to public `NVlabs/COMPASS`. Existing public repo already references OSMO dataset names (`record.py:147` `--dataset-name`). + +- [x] Inventory OSMO-specific files in internal repo (launch scripts, configs, manifests) — landed in `1253f2a` +- [x] Sanitize for public release (strip internal paths, secrets, unsupported clusters) — `1253f2a` (initial) + `d0275cc` (HF asset sources, no internal defaults) +- [x] Decide landing directory: `osmo/` +- [x] Port docs / README section explaining OSMO submission flow — `osmo/README.md` + root README section +- [x] Thread `--embodiment` / `--environment` through train workflow (so multi-embodiment sweeps don't require YAML hand-edits) — `d605901` +- [ ] Smoke test: launch one specialist training run end-to-end on OSMO +- [ ] Smoke test: launch one distillation run end-to-end on OSMO +- [ ] PR: + +**Internal source:** `gitlab-master.nvidia.com/ml_nav/compass` +**Commits on integration branch:** `1253f2a`, `d0275cc`, `d605901` + +## 2 & 3. Isaac Lab 3.0+ upgrade (in 1.6) **+** NuRec official support (post-1.6) — P0/⚪ + +**1.6 scope:** only Bucket A (Isaac Lab 3.0 API migration) ships, landed as `22b25ef`. **NuRec PR-2 (Buckets B+E+H) is deferred** to a clean post-1.6 PR off `main` — the source branch `origin/samc/support_nurec_assets_isaaclab_3.0` (5 commits, ~1818 LOC) bundles unrelated work and will not be merged whole. The deferred PR-2 plus the Buckets C/D/F/G follow-up land after the v1.6.0 tag. + +### Branch decomposition (cherry-pick plan) + +| Bucket | What it is | LOC | Lands in | +|--------|-----------|-----|----------| +| **A** | Isaac Lab 3.0 API migration: env_cfg signatures, ActionTerm import path, Warp interop (`wp.to_torch()`), PhysxCfg restructure, separate `write_root_pose_to_sim_index` / `write_root_velocity_to_sim_index`, `num_rerenders_on_reset`, **global quaternion convention flip in `environments.py` (wxyz → xyzw)**. Touches env_cfg.py, MDP terms, robots, scene_assets, *_env_cfg.py, pyproject.toml, Dockerfile.rl. | ~150 | **PR-1** | +| **B** | NuRec real2sim asset support: 41-line `environments.py` block (NuRec USD path + occupancy entry + `nova_carter_galileo_nurec` cfg), `configs/{train,eval}_config_real2sim.gin`, `run.py` registration. | ~95 | **PR-2** | +| **C** | New file `compass/utils/multi_camera_video_recorder.py` (689 LOC). General-purpose `gym.Wrapper` recording viewport + camera side-by-side. | 689 | Deferred | +| **D** | `residual_ppo_trainer.py` video upload dedup (`_find_video_files`, rewritten `_upload_video`, dedup sets). Pairs with C. | ~140 | Deferred | +| **E** | `occupancy_map.py` 429-line refactor: origin-convention support (top-left vs ROS bottom-left) **+** `precompute_valid_poses()` that buffers obstacles via scipy.ndimage and caches valid start/goal locations. **NOT USD-derived** — independent of #5. | 429 | **PR-2** (origin convention is required to parse NuRec's occupancy entry; precompute rides along, gated by opt-in flag) | +| **F** | Browser-compatible video: ffmpeg H.264 re-encode + `+faststart`, libx264 preset/crf tuning. Commits `ede049d` and `c34645f`. | ~80 | Deferred | +| **G** | Debug images / logger: `compass/utils/logger.py:74-91` adds `log_image()`; trainer `_save_debug_images()` + `_create_image_grid()` write PNG grids of obs every N iters. Commit `bafac90`. | ~190 | Deferred | +| **H** | Cleanups (run.py duplicated lines 244-246/247-250; `EnvSceneAssetCfgMap['nova_carter-galileo']` overrides existing `warehouse_multi_rack` mapping). | small | **PR-2** | + +### PR-1 — Isaac Lab 3.0 API migration (Bucket A) — P0 + +Foundation. No new features; pure 2.1 → 3.0 compat. Off `main`. + +- [x] Extract A-only hunks from commit `3e6dcd9` — landed in `22b25ef` ("Migrate mobility_es extension to Isaac Lab 3.0 API") +- [x] Update version pins: README badge + `compass/rl_env/README.md` install instructions bumped to `v3.0.0-beta1` — part of `22b25ef` +- [ ] Survey Isaac Lab 2.1 → 3.0 release notes / migration guide; confirm branch covers them +- [ ] Update `docs/handbook/extending.md` bare-metal install pin +- [ ] Update `docker/Dockerfile.distillation` base image if needed (`Dockerfile.rl` already on branch) +- [ ] **Reviewer spot-check**: quaternion convention flip across all rows of `environments.py` (wxyz → xyzw) — confirm no Y-up vs Z-up assumption breaks +- [ ] Re-validate USD assets load under 3.0 (`compass/rl_env/exts/mobility_es/mobility_es/usd/`) +- [ ] Smoke test: one short training per supported embodiment (H1, Carter, Spot, G1, Digit) +- [ ] PR: + +### PR-2 — NuRec real2sim assets + occupancy_map plumbing (Buckets B + E + H fixes) — ⚪ **deferred to post-1.6** + +Off PR-1. NuRec asset support, the `occupancy_map.py` refactor it depends on, and cleanups in the same files. Bucket A (PR-1) already in 1.6 via `22b25ef`; PR-2 ships as a clean follow-up PR off `main` after v1.6.0. + +- [ ] Cherry-pick `environments.py` NuRec block + `configs/train_config_real2sim.gin` + `configs/eval_config_real2sim.gin` +- [ ] Cherry-pick `compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py` refactor (origin convention + `precompute_valid_poses`) +- [ ] **Cleanup**: collapse duplicated `run.py` lines 244-246 / 247-250 +- [ ] **Cleanup**: register `'nova_carter-galileo'` as a *new* `EnvSceneAssetCfgMap` key alongside the existing `warehouse_multi_rack` (do not overwrite — fixes commit `86f9664`) +- [ ] Add NuRec asset section to `docs/handbook/extending.md` (or a new handbook page if the section grows) +- [ ] Add NuRec entry under "External assets that must be downloaded manually" in `README.md` +- [ ] Smoke test: training run with NuRec asset +- [ ] Smoke test: confirm a non-NuRec run on a `warehouse_multi_rack` scene still works (regression check) +- [ ] PR: + +### Deferred to follow-up PR (Buckets C + F + D + G) + +Not on the release critical path. Ship after the release tag as one coherent observability PR. + +- Multi-camera video recorder (`compass/utils/multi_camera_video_recorder.py`) — **C** +- Browser-compat video re-encoding (ffmpeg H.264 + faststart) — **F** +- Trainer video upload dedup (viewport + combined uploads) — **D** +- Debug image grid logging (`logger.log_image()` + `_save_debug_images()`) — **G** + +Rationale: C + F record videos to disk; without D's wandb upload plumbing they're half-baked. G is the same observability stack. All four ship together post-release. Also revisit `bafac90`'s `debug_image_interval` default at that time so output dirs don't fill up. + +**Branch:** `origin/samc/support_nurec_assets_isaaclab_3.0` (5 commits, ~1818 LOC) +**Predecessor branch:** `origin/samc/support_nurec_assets_isaaclab_2.3.1` (kept for reference; not merged) +**Owners:** @samc (NuRec) + @liuw (Isaac Lab integration review) + +## 4. Agentic skills for automatic model training — P1 + +Land the Claude Code skill that automates training-loop execution. Migration phase done — `.claude/skills/compass/SKILL.md` plus the two helper scripts (`scripts/sage10k_search.py`, `scripts/sage10k_to_usd.py`) are in the public repo and document the SAGE-10k → USD → register → train pipeline. + +- [x] Identify scope: Claude Code skill (markdown + YAML frontmatter + helper scripts). +- [x] Decide landing directory: `.claude/skills/compass/`. +- [x] Sanitize for public release. +- [x] Hook into training entry points (`run.py`, evaluation, OSMO submission). +- [x] Document trigger commands and expected behavior (`docs/handbook/agentic.md`). +- [ ] Demo: end-to-end automated specialist training using one skill — covered by **#10** verification. +- [ ] Demo: SAGE-driven training using the skill — covered by **#10** verification. +- [ ] PR: + +**Continued in:** #10 (refresh for docker-as-venv + new specialty skills). The two demo boxes flip 🟢 once #10's verification step runs them. + +## 5. Auto OMap generation from USDs — P1 + +Replace the manual occupancy-map authoring step with a USD-derived generator so SAGE-driven training (and any new scene) can ramp up without hand-tuned omaps. The auto-gen flow is documented at https://nvlabs.github.io/COMPASS/docs/omap.html and consumed by `compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py`. + +> **Relationship with #2&3:** Complementary, not overlapping. The NuRec branch's `occupancy_map.py` change is precomputation + origin convention (loads pre-baked YAML faster), not USD generation. #5 is genuinely separate work that produces the YAML automatically from a USD. + +- [x] Add CLI/script `scripts/generate_omap_from_usd.py` that produces the omap PNG+YAML directly from a USD scene (wraps `isaacsim.asset.gen.omap.bindings._omap.Generator`) +- [x] Update `compass/rl_env/exts/mobility_es/mobility_es/utils/occupancy_map.py` so a scene without an `OMAP_PATHS` entry auto-discovers `/omap/occupancy_map.yaml` (no breaking change to existing entries) +- [x] Update `compass/rl_env/README.md` and `.claude/skills/compass/SKILL.md` to point at the auto-generation flow +- [x] Verify generation + collision-free sampling on representative USDs: + - `office.usd` — ✅ 200/200 free samples in unoccupied regions + - `combined_simple_warehouse/combined.usd` (default training scene) — ✅ 253/300 free samples; visually free dots avoid obstacles + - `sample_small_footprint_one_rack_obst_sdg.usd` — ✅ 145/300 free samples; same +- [ ] Regenerate OMaps for all bundled scenes (optional follow-up; current loader auto-discovers when no `OMAP_PATHS` entry) +- [ ] Measure training-throughput delta vs. manual OMaps; record in benchmark report +- [ ] PR: + +**Branch:** `liuw/auto_omap_from_usd` (off `liuw/agentic_skills_migration`) +**Key fix:** wait on `omni.usd.get_context().get_stage_loading_status()` until `to_load == 0` before invoking `generate2d()` — without this, kit crashes on USDs with external references (e.g. `combined_simple_warehouse` referencing `galileo_lab.usd`) when the omap generator queries fabric for prims whose references are still resolving. Mirrors the pattern Isaac Sim's own `isaacsim.asset.gen.omap` tests use. + +## 6. GitHub Pages docs site — P1 + +End-to-end docs site auto-deployed from `main/docs/` via GitHub Actions. Academic landing stays at `nvlabs.github.io/COMPASS/`; new **Sphinx handbook with the NVIDIA theme** (matching `agentic_model_training/docs/` and the rest of NVIDIA OSS) serves at `nvlabs.github.io/COMPASS/docs/`. Replaces the hand-served `gh_page` branch. + +- [x] Stack decided: **Sphinx 7.x + nvidia-sphinx-theme + myst-parser** (markdown survives; matches NVIDIA house style) +- [x] Source location: `main/docs/` (so doc edits go through normal PRs); `gh_page` left as a frozen archive +- [x] URL layout: academic at `/`, handbook at `/docs/` +- [x] Migrate `gh_page` → `docs/project_page/` (264 files; mp4/png LFS-tracked, rest as Git blobs) — landed in commit `305c3a1` +- [x] Add `docs/handbook/{conf.py, Makefile, requirements.txt, _static/, docs/}` with `{toctree}` nav (Installation / Workflows / Deployment / Reference) +- [x] Transclude existing READMEs (Docker, OSMO, ROS2, mobility_es, CONTRIBUTING) via MyST `` ```{include} `` directives; no copy-paste of content +- [x] Add Documentation CTA on academic landing (`docs/project_page/index.html` → `./docs/`) +- [x] Wire `.github/workflows/docs.yml` (`make html` / `sphinx-build -W`, copy academic landing to root, deploy via `actions/deploy-pages@v4`) +- [ ] Local build verification: `make html` runs clean; all 16 handbook pages render; both `/` and `/docs/` serve correctly from `_site/` +- [ ] Repo settings: **Settings → Pages → Source = "GitHub Actions"** (one-time owner action; documented in PR description) +- [ ] Push to main + watch first deploy succeed +- [ ] PR: + +**Branch:** `liuw/docs_site` (off `liuw/dev_environment`, latest in the stack). +**Status:** files written; local Sphinx build pending; Pages source switch + push pending. + +## 7. Docker-as-venv dev environment — P1 + +Quality-of-life: cut first-time UX from "30–60 min, 6 manual steps" to **"3 commands, ~3 min"**, and make the steady-state dev loop feel like a Python venv (host-side editor, host-side shell, but every `python`/`pip`/`tensorboard` invocation transparently routed through the container via `source ./docker/activate`). Reuses `docker/Dockerfile.rl` (single image, +5 lines). Detailed plan at [`dev_env_plan.md`](dev_env_plan.md). + +- [x] Add `docker/run.sh` (subcommands: build / assets / up / down / exec / shell / status) +- [x] Add `docker/activate` (sourceable — venv-like; shim PATH for python/pip/tensorboard/etc., CWD translation, deactivate) +- [x] Add `docker/prepare_assets.sh` (USDs + X-Mobility ckpt → `./assets/`, cache-aware) +- [x] Add `docker/README.md` (subcommand reference, multi-checkout / multi-GPU notes, troubleshooting) +- [x] Modify `docker/Dockerfile.rl`: install COMPASS at `/workspace/COMPASS` (so `/workspace/isaaclab` survives the bind-mount); add a `python`/`python3` wrapper that exec's Isaac Sim's bundled `python.sh` +- [x] Update `.dockerignore` (`./assets/`, `./.cache/`, `./.git/`) and `.gitignore` (`/assets/`, `/.cache/`) +- [x] Update root `README.md` to lead with the Docker quick-start; bare-metal moved under "Manual install" +- [ ] Verify: `./docker/run.sh build && ./docker/run.sh assets && source ./docker/activate && python run.py … --num_envs 1 --headless` reaches first PPO iteration +- [ ] Verify: `git commit -s` from an activated shell triggers pre-commit through the shim and signs cleanly +- [ ] Verify: `osmo/run_osmo.py train` still works against the same image (regression) +- [ ] PR: + +**Branch:** `liuw/dev_environment` (off `liuw/auto_omap_from_usd`, latest in the stack). +**Status:** files written; verification + commit pending. + +## 8. Pre-release leak audit + sanitization — P0 + +Scrub all internal-only references before tagging 1.6. Inventory in the +planning round; the meaningful work is in the OSMO entry script. + +- [x] OSMO workflows: replace `groot_mobility_rl_es_usds` dataset input with HF download `nvidia/COMPASS / compass_usds.zip` (3 YAMLs) +- [x] OSMO workflows: replace `wandb artifact get …base_policy_ckpt…` with HF download `nvidia/X-Mobility / x_mobility-nav2-semantic_action_path.ckpt` (3 YAMLs) +- [x] `osmo/run_osmo.py`: drop `nvidia-isaac` wandb-project defaults (`compass_rl_enhance`, `afm_train`); make `--wandb-project` required; drop `--base-policy-ckpt` flag (workflow now hardcodes the HF source) +- [x] Update `docs/handbook/osmo.md` to reflect HF-sourced assets + required `--wandb-project` +- [ ] `ros2_deployment/compass_navigator/setup.py`: review maintainer attribution (flag for @liuw; team alias preferred) +- [ ] OSMO smoke test: resubmit `compass_rl_es_g1_official` with the rebuilt image; confirm HF download steps succeed and training reaches first PPO iter +- [x] Repo-wide grep gate: `grep -rnE "nvidia-isaac/|afm_train|groot_mobility_rl_enhance|afm_rl_enhance" --include='*.py' --include='*.sh' --include='*.yaml' --include='*.gin' --include='*.html' .` returns no live-source hits. (Dropped `groot_mobility_rl_es_usds` from the pattern — it's the directory name inside the public HF `compass_usds.zip` and is correctly referenced by `osmo/workflows/*.yaml` after unzipping.) +- [ ] Distill `release_tracker.md` + `dev_env_plan.md` into `CHANGELOG.md` and remove from the repo at tag time (existing gate row) +- [ ] PR: + +**Branch:** `liuw/sanitize_for_public` (off `liuw/docs_site`, latest in the stack). + +## 9. CI/CD setup + dependency pinning — P1 + +Bring the public repo up to a "first-line" CI posture before tagging: + +- [x] Add `.github/workflows/pre-commit.yml` (yapf / pylint / nbstripout / clang-format / large-files / trailing-whitespace / EOF / requirements-txt-fixer) +- [x] Pin `requirements.txt` to versions verified inside `compass-rl:latest` (the image used for the just-passed OSMO smoke). 17 unpinned → 17 pinned; `diffusers==0.29.2` was already pinned. +- [ ] First run of the workflow on PR-9: confirm pre-commit passes `--all-files` against the current branch. If legacy violations surface, fix in a follow-up commit on the same branch. +- [ ] Decide later whether to add a Sphinx-handbook `linkcheck` job (out of scope for now; tracked under #6 docs). +- [ ] PR: + +**Branch:** `liuw/ci_setup` (off `liuw/sanitize_for_public`, latest in the stack). + +## 10. Agentic skills refresh + new onboarding skills — P1 + +Refresh the existing `compass` skill for the new docker-as-venv flow (#7) and add three onboarding-focused specialty skills following the **hybrid front-door** pattern (`compass` is the umbrella; `compass-deploy` / `compass-debug` / `compass-newembodiment` are narrow siblings the auto-router picks unambiguously). + +- [x] Update `.claude/skills/compass/SKILL.md` for docker-as-venv (drop conda wrappers; new prereq check; new setup recipe; OSMO example aligned with #8). Add "Specialty skills" front-door section. Soften MUST-style rules with explanatory why. +- [x] Pre-emptive progressive-disclosure split: extract "Setup SAGE Local (Advanced)" to `.claude/skills/compass/references/setup-sage-local.md`. +- [x] Add `.claude/skills/compass-deploy/SKILL.md` (ckpt → ONNX → TRT → ROS2 launch scaffold). +- [x] Add `.claude/skills/compass-debug/SKILL.md` + bundled `scripts/compass_status.sh` (8 diagnostic checks; markdown table; --deep + --ckpt flags). +- [x] Add `.claude/skills/compass-newembodiment/SKILL.md` (interactive robot onboarding; parses pre-supplied user input; AskUserQuestion only for missing fields; diff-then-confirm before writing). +- [x] Update `docs/handbook/agentic.md` with four-skill index ("Pick the right skill" matrix at the top). +- [ ] Live invocation test in a fresh Claude Code session for each skill; confirm prereq check passes inside an activated shell. +- [ ] Run the two #4 demo flows (specialist training + SAGE-driven training) using `/compass`; flip #4 demo boxes 🟢 on success. +- [ ] (Post-PR follow-up) Run `/skill-creator`'s description-optimization loop against all four skills; apply the highest-scoring descriptions on a held-out trigger eval split. +- [ ] PR: + +**Branch:** `liuw/skills_enhancement` (off `liuw/ci_setup`, latest in the stack). + +## 11. Multi-GPU PPO training + perf instrumentation — TBD priority + +8-GPU distributed residual-RL training (torchrun + manual all-reduce for gradients / KL / metrics), per-stage timing instrumentation, supporting OSMO 8-GPU workflow, and a perf-analysis report. + +Scope (commit `9f478af`): +- `run.py`, `compass/residual_rl/ppo.py`, `compass/residual_rl/residual_ppo_trainer.py`: `--distributed`, `dist.init_process_group(nccl)`, per-rank device pinning, rank-0 gating on logger / ckpt / video / episode-log, weighted per-rank log aggregation, manual gradient all-reduce (AVG) BEFORE clip, KL all-reduce before LR adaptation, metric all-reduce, diagnostics dict for `ppo/learning_rate` / `kl_mean` / `entropy` / `action_std_mean`. +- `osmo/workflows/rl_es_train_8gpu_workflow.yaml`: 8-GPU / 80-CPU / 800-GiB on ovx-l40; train via `torch.distributed.run --nproc_per_node=8 run.py --distributed --num_envs 32` (256 total envs); single-process eval. +- `osmo/run_osmo.py`: `--num-gpus` flag (choices 2, 8) routes to matching workflow YAML. + +- [x] Multi-GPU code path landed (`9f478af`) +- [x] OSMO 8-GPU workflow YAML +- [x] **Release-scope decision:** ship in 1.6 (settled by squash strategy — its commit is on the integration branch and goes into the squash unless explicitly excluded) +- [ ] Numerical-equivalence check vs single-GPU baseline (loss curve / success-rate parity within seed noise) +- [ ] 8-GPU OSMO smoke run reaches first PPO iter and produces ckpts +- [ ] 2-GPU path smoke (the other `--num-gpus` choice) +- [ ] Document `--distributed` in handbook / OSMO docs +- [ ] PR: + +**Branch:** currently sits at the tip of `liuw/training_time_improve` (this commit is the branch name's eponym). Needs to be carved into its own PR or rolled into the release stack. + +## Pre-release gates + +- [🟡] **No-regression benchmark** — 4 embodiments × 5 scenes, eval via `osmo/run_benchmark.py`. v1.5.0 baseline numbers not recovered; user assesses go/no-go manually. + - [x] Sanitize and land internal `benchmark.py` (113 lines; hardcodes `nvcr.io/nvstaging/isaac-amr/groot_mobility_rl_enhance` registry + `afm_rl_enhance` defaults). Landed as `osmo/run_benchmark.py` on `liuw/benchmark_port`: Apache-2.0 header, `--registry-prefix` (with `$COMPASS_OSMO_REGISTRY` fallback), `--wandb-project-name` now `required=True`, workflow path resolved relative to `osmo/`. Reuses existing `osmo/workflows/rl_es_eval_workflow.yaml`. + - [x] Define the regression matrix: baked into `osmo/run_benchmark.py` defaults — embodiments `{g1, h1, spot, carter}` × environments `{simple_office, warehouse_single_rack, warehouse_multi_rack, combined_single_rack, combined_multi_rack}` (5 scenes/embodiment, single seed). One invocation per embodiment fires 5 OSMO eval jobs. + - [⚪] Capture baseline numbers from v1.5.0 — deferred; user assesses manually. The 1.6 results below become the new published baseline. + + **Eval configuration**: all jobs ran with `compass_release_1_6_relaxed:c87052af` (built off `liuw/benchmark_port` with `heading_threshold` flipped from `0.1` → `math.pi` in `compass/rl_env/exts/mobility_es/mobility_es/mdp/termination.py:33`). Source ships with the default `0.1`. **Release notes must document this.** + + #### iter-500 multi-GPU (`*_8gpu_lrcap2e3` runs, pool `isaac-dev-l40-03`) + + Goal-reached rate / fall-down rate per cell: + + | Embodiment | simple_office | warehouse_single_rack | warehouse_multi_rack | combined_single_rack | combined_multi_rack | **avg goal** | **avg fall** | + |---|---:|---:|---:|---:|---:|---:|---:| + | carter | 0.619 / 0.377 | **0.945** / 0.050 | 0.728 / 0.178 | 0.861 / 0.134 | 0.889 / 0.100 | **0.808** | **0.168** | + | g1 | 0.494 / 0.503 | 0.878 / 0.077 | 0.828 / 0.106 | 0.833 / 0.164 | 0.805 / 0.183 | 0.768 | 0.207 | + | spot | 0.569 / 0.425 | 0.800 / 0.195 | 0.766 / 0.161 | 0.811 / 0.188 | 0.692 / 0.305 | 0.728 | 0.255 | + | h1 | 0.422 / 0.578 | 0.903 / 0.077 | 0.714 / 0.250 | 0.784 / 0.208 | 0.731 / 0.242 | 0.711 | 0.271 | + + Per-embodiment `weighted_travel_time` average (lower is better): carter **2,293** · spot 3,675 · g1 4,356 · h1 4,451. + + Headlines: + - carter wins on all three axes (wheeled platform). + - `simple_office` is the universal failure mode for bipeds — 42-57% success, 42-58% fall rate; consistent with the 32.8%-free omap density we measured. + - `warehouse_single_rack` is the universally easiest scene (78-95% success). + - `goal_reached + fall_down ≈ 1.0` everywhere — `fall_down` is the dominant failure mode (no meaningful time-out outcomes). + + #### iter-1000 single-GPU baseline (`*_baseline` runs, pool `groot-l40-04`) + + All 20 cells completed. Wandb project `compass_release_1.6_benchmark`, run-name pattern `bm___release_1_6_iter1000_1gpu`. + + Goal-reached rate / fall-down rate per cell: + + | Embodiment | simple_office | warehouse_single_rack | warehouse_multi_rack | combined_single_rack | combined_multi_rack | **avg goal** | **avg fall** | + |---|---:|---:|---:|---:|---:|---:|---:| + | carter | 0.630 / 0.369 | **0.950** / 0.042 | 0.792 / 0.145 | 0.872 / 0.111 | 0.887 / 0.092 | **0.826** | **0.152** | + | g1 | 0.502 / 0.489 | 0.850 / 0.102 | 0.569 / 0.314 | 0.841 / 0.155 | 0.812 / 0.178 | 0.715 | 0.248 | + | spot | 0.589 / 0.408 | 0.794 / 0.206 | 0.781 / 0.188 | 0.808 / 0.191 | 0.653 / 0.345 | 0.725 | 0.268 | + | h1 | 0.500 / 0.455 | 0.812 / 0.111 | 0.506 / 0.338 | 0.795 / 0.175 | 0.783 / 0.138 | 0.679 | 0.243 | + + Per-embodiment `weighted_travel_time` average (lower is better): carter **2,050** · spot 3,992 · g1 5,114 · h1 5,157. + + #### Side-by-side comparison: iter-500 multi-GPU vs iter-1000 single-GPU + + **Per-embodiment averages (Δ = iter-1000 − iter-500):** + + | Emb | iter-500 goal | iter-1000 goal | Δ goal | iter-500 fall | iter-1000 fall | Δ fall | iter-500 wtt | iter-1000 wtt | + |---|---:|---:|---:|---:|---:|---:|---:|---:| + | carter | 0.808 | **0.826** | **+0.018** | 0.168 | **0.152** | **−0.016** | 2,293 | **2,050** | + | g1 | **0.768** | 0.715 | −0.053 | **0.207** | 0.248 | +0.041 | **4,356** | 5,114 | + | spot | **0.728** | 0.725 | −0.003 | **0.255** | 0.268 | +0.013 | **3,675** | 3,992 | + | h1 | **0.711** | 0.679 | −0.032 | 0.271 | **0.243** | −0.028 | **4,451** | 5,157 | + + **Headlines:** + - **carter improves on all axes at iter-1000 single-GPU** — converges fastest, benefits from more training. + - **Bipeds (g1, h1) are roughly on-par or slightly regress at iter-1000 single-GPU.** Average drift is single-digit pp; per-scene drift is dominated by `warehouse_multi_rack` (g1: −25.9 pp goal, +20.8 pp fall; h1: −20.8 pp goal, +8.8 pp fall). Other scenes are stable. + - **Validates §11 multi-GPU PPO numerical equivalence in spirit**: 8 GPUs × 500 iter ≈ 1 GPU × 1000 iter in samples-seen, and the resulting policies are within seed-noise on most cells. The `warehouse_multi_rack` divergence for bipeds is worth flagging in release notes but does not block tag — neither config is strictly worse across the matrix. + - **`simple_office` remains the universal weakness** — bipeds in the 50-60% range, carter at 63%. Tighter scene + bipedal locomotion is the dominant failure mode regardless of training config. +- [ ] All P0 items 🟢 +- [ ] CHANGELOG.md `[1.6.0]` entry drafted (Added / Changed / Fixed / Removed) +- [ ] Version bump committed +- [ ] X-Mobility wheel updated if needed +- [ ] Pre-commit clean: `pre-commit run --all-files` +- [ ] Docs site builds without warnings +- [ ] Release notes drafted + +## Release + +- [ ] Tag `v1.6.0` and push +- [ ] GitHub Release published with notes +- [ ] HuggingFace assets (USDs, checkpoints) updated/uploaded if changed +- [ ] Internal announcement posted + +## Out of scope / deferred + +_Move items here with a one-line reason once cut. Empty at planning start._ + +## Notes + +- DCO sign-off (`git commit -s`) is required for every commit — see `CONTRIBUTING.md` +- Internal repo: `gitlab-master.nvidia.com/ml_nav/compass` +- Public repo: `github.com/NVlabs/COMPASS` +- Existing `gh_page` branch hosts the academic project page (Bulma static site) — do not confuse with the new docs site diff --git a/requirements.txt b/requirements.txt index d48bfc8..cc93b69 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,18 +1,18 @@ diffusers==0.29.2 -einops -gin-config -h5py -matplotlib -moviepy -msgpack -numpy -onnx -onnxruntime-gpu -pandas -pytorch-lightning -timm -torcheval -transformers -wandb -wheel -zmq +einops==0.8.2 +gin-config==0.5.0 +h5py==3.16.0 +matplotlib==3.10.8 +moviepy==2.2.1 +msgpack==1.1.2 +numpy==1.26.4 +onnx==1.20.1 +onnxruntime-gpu==1.25.1 +pandas==3.0.1 +pytorch-lightning==2.6.1 +timm==1.0.26 +torcheval==0.0.7 +transformers==4.57.6 +wandb==0.25.1 +wheel==0.47.0 +zmq==0.0.0 diff --git a/ros2_deployment/ISAACSIM_README.md b/ros2_deployment/ISAACSIM_README.md deleted file mode 100644 index 5343041..0000000 --- a/ros2_deployment/ISAACSIM_README.md +++ /dev/null @@ -1,236 +0,0 @@ -# Compass Navigator ROS2 Deployment in ISAAC SIM - -## Overview - -**Standalone ROS2 deployment** for Compass Navigator with Isaac Sim integration. - -### What's Included - -**`compass_container.py`** - Container management script that: -- Manages **two separate Docker containers**: Compass Navigator and Isaac Sim -- Controls container lifecycle: start, stop, enter, and monitor both containers -- Handles TensorRT model conversion automatically -- Launches Isaac Sim container with ROS2 bridge -- Provides easy container entry and debugging - -**`Dockerfile.deploy`** - Creates a container with: -- ROS2 Humble + CUDA 11.8 + TensorRT -- Compass Navigator package and dependencies -- Non-root user setup for development -- Proper FastDDS configuration for inter-container communication - -## Quick Setup & Run - -### 1. Build the Container -```bash -export COMPASS_PATH=/path/to/compass -export HUGGINGFACE_TOKEN=hf_your_token_here -cd $COMPASS_PATH/ros2_deployment -./build_compass_docker.sh -``` - -### 2. Launch Isaac Sim (Terminal 1) -```bash -./compass_container.py launch_isaac_sim -``` -- **In Isaac Sim GUI:** Go to `Windows` → `Examples` → `Robotics Examples` → `ROS2` → `Navigation` -- **Load a robot scene** (e.g., Carter) -- **Press Play (▶️)** to start simulation - -### 3. Launch Compass Navigator with GUI (Terminal 2) -```bash -./compass_container.py --rviz launch_nav -``` -- Automatically converts ONNX to TensorRT (first time only) -- Builds ROS2 workspace and launches navigator -- Opens RViz2 with robot visualization - -### 3.b Navigate the Robot -- **In RViz2:** Use the **2D Nav Goal** tool in the toolbar -- **Click anywhere on the map** to set navigation targets -- **Robot navigates autonomously** using AI-powered path planning - -## 4. Alternative: Headless Mode (No GUI) - -### 4.a Launch Compass Navigator -```bash -./compass_container.py launch_nav -``` -- Runs navigator without RViz2 GUI (saves resources) -- All ROS2 topics still available for external tools - -### 4.b Send Navigation Goals -```bash -# Enter the compass container -./compass_container.py enter - -# Inside container, publish a goal pose -ros2 topic pub --once /goal_pose geometry_msgs/msg/PoseStamped " -header: - frame_id: 'odom' -pose: - position: - x: 2.0 - y: 0.0 - z: 0.0 - orientation: - x: 0.0 - y: 0.0 - z: 0.0 - w: 1.0 -" - -# Monitor navigation progress -ros2 topic echo /cmd_vel --once # Check robot commands -ros2 topic echo /chassis/odom --once # Check robot position -or visualize in Isaac-Sim -``` - -## Container Commands - -```bash -# Compass Navigator container lifecycle -./compass_container.py start # Start compass container headless (background) -./compass_container.py --rviz start # Start compass container with RViz GUI -./compass_container.py enter # Enter running compass container -./compass_container.py stop # Stop compass container -./compass_container.py logs # View compass container logs -./compass_container.py status # Check compass container status - -# Isaac Sim container (separate) -./compass_container.py launch_isaac_sim # Start Isaac Sim container (interactive) - -# Combined launch options -./compass_container.py launch_nav # Quick headless launch -./compass_container.py --rviz launch_nav # Quick launch with RViz GUI -./compass_container.py launch_nav --force-trt-conversion # Rebuild TensorRT engine (headless) -./compass_container.py --rviz launch_nav --force-trt-conversion # Rebuild engine + GUI - -# Development -./compass_container.py enter # Enter headless container -./compass_container.py --rviz enter # Enter container with RViz support - -# Troubleshooting -./compass_container.py debug_network_setup # Debug ROS2 network setup -``` - -## Container Lifecycle - -### Compass Navigator Container -- **Starts in detached mode** (runs in background) -- **Persists after `launch_nav` command exits** - container keeps running -- **Runs ROS2 nodes and RViz2** inside the container -- **Must be manually stopped** with `./compass_container.py stop` - -```bash -./compass_container.py launch_nav -# Command exits, but container + ROS2 nodes continue running - -./compass_container.py status # Check if still running -./compass_container.py enter # Access running container -./compass_container.py stop # Stop container -``` - -### Isaac Sim Container -- **Starts in interactive mode** (blocks terminal) -- **Container exits when Isaac Sim GUI closes** -- **Auto-removes itself** when terminated -- **Command blocks until you close Isaac Sim** - -```bash -./compass_container.py launch_isaac_sim -# Command blocks, shows Isaac Sim GUI -# Close Isaac Sim → container exits → command exits -# Container is automatically removed -``` - -### Typical Workflow -```bash -# Terminal 1: Start Isaac Sim (interactive, blocks) -./compass_container.py launch_isaac_sim - -# Terminal 2: Start Compass (detached, returns immediately) -./compass_container.py launch_nav - -# Use both systems... - -# Terminal 1: Close Isaac Sim GUI (container auto-exits) -# Terminal 2: Stop Compass container -./compass_container.py stop -``` - -## Troubleshooting - -**No GUI / Black screen:** -```bash -# X11 permissions are checked automatically when launching Isaac Sim -# If launch fails with X11 errors, fix permissions: -xhost +local:docker -export DISPLAY=:0 -``` - -**Topics not visible between containers:** -```bash -# Verify ROS2 domain consistency -echo $ROS_DOMAIN_ID # Should be same (0 by default) -``` - -**Package not found errors:** -```bash -# Force workspace rebuild -./compass_container.py enter -# Inside container: rm -rf build install log && colcon build --symlink-install -``` - -**Model updated (need TensorRT conversion):** -```bash -# If you manually changed the ONNX model in assets/models/ -./compass_container.py launch_nav --force-trt-conversion -``` - -**Manual workspace build (inside container):** -```bash -cd /home/compassuser/compass_ws -source /opt/ros/humble/setup.bash -colcon build --symlink-install -``` - -**Build Issues:** - -**HUGGINGFACE_TOKEN not set:** -```bash -# Get token from https://huggingface.co/settings/tokens after requesting access to nvidia/COMPASS -export HUGGINGFACE_TOKEN=hf_your_token_here -./build_compass_docker.sh -``` - -**TensorRT conversion fails:** -```bash -# Check GPU memory usage -nvidia-smi -# Stop other GPU processes if needed -./compass_container.py stop -./compass_container.py launch_nav --force-trt-conversion -``` - -**ROS2 Communication Issues:** - -**Robot doesn't respond to navigation commands:** -```bash -# Verify Isaac Sim is running and scene is playing (Play ▶️ button pressed) -# Inside compass navigator container: -./compass_container.py enter -# Inside container: -ros2 topic list | grep -E "(cmd_vel|odom|image)" -ros2 topic echo /cmd_vel --once # Should show navigation commands when you set goals -``` - -**Network troubleshooting:** -```bash -# Automated network diagnostics (recommended) -./compass_container.py debug_network_setup -# This checks environment variables, network mode, and ROS2 topic discovery in both containers - -# Manual checks (if needed): -docker inspect compass-ros2-navigator | grep NetworkMode # Should show: "NetworkMode": "host" -``` diff --git a/ros2_deployment/README.md b/ros2_deployment/README.md deleted file mode 100644 index 0fcf496..0000000 --- a/ros2_deployment/README.md +++ /dev/null @@ -1,38 +0,0 @@ -# COMPASS ROS2 Deployment - -This directory contains ROS2 packages for deploying **COMPASS** across both simulation and real-world environments. - -## Provided ROS2 Nodes - -- **compass_inference**: Consumes camera images, target poses, and robot speed inputs, then outputs velocity commands by running TensorRT inference with COMPASS engines. -- **obj_target_generator**: Receives object localization bounding boxes and generates navigation target poses for the COMPASS navigator. - -These nodes enable a variety of integration workflows, including the following examples: - ---- - -## 1. Isaac Sim Integration - -The `compass_navigator` package is fully compatible with [NVIDIA Isaac Sim](https://developer.nvidia.com/isaac-sim), leveraging its robotics environments and ROS2 bridge for seamless simulation. - -For detailed setup instructions and a step-by-step Isaac Sim integration guide, see [ISAACSIM_README.md](./ISAACSIM_README.md) in this directory. - ---- - -## 2. Zero-Shot Sim2Real Transfer - -The **compass_inference** node can also be deployed directly on real robots, enabling a seamless transition from simulation to real-world operation. By integrating visual SLAM solutions such as [cuVSLAM](https://nvidia-isaac-ros.github.io/concepts/visual_slam/cuvslam/index.html) for robot state estimation, the COMPASS model can support zero-shot Sim2Real transfer. - -https://github.com/user-attachments/assets/141af4f6-c915-4254-b6a0-00706e4aea5f - ---- - -## 3. Object Navigation Integration - - -By integrating an object localization module (e.g., [Locate3D](https://locate3d.atmeta.com/)), we can also enable object navigation with COMPASS. The **obj_target_generator** node can convert localized object bounding boxes into navigation goals, allowing the robot to autonomously approach specified objects. - - -https://github.com/user-attachments/assets/3928e9fd-f78d-4b8e-8bbc-9932b386ae6b - ---- diff --git a/ros2_deployment/compass_container.py b/ros2_deployment/compass_container.py index c7f625d..83bc963 100755 --- a/ros2_deployment/compass_container.py +++ b/ros2_deployment/compass_container.py @@ -3,7 +3,6 @@ # Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 - # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # diff --git a/ros2_deployment/compass_navigator/compass_navigator/__init__.py b/ros2_deployment/compass_navigator/compass_navigator/__init__.py index 7ab23ee..3159bfe 100644 --- a/ros2_deployment/compass_navigator/compass_navigator/__init__.py +++ b/ros2_deployment/compass_navigator/compass_navigator/__init__.py @@ -12,4 +12,3 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - diff --git a/ros2_deployment/compass_navigator/compass_navigator/obj_target_generator.py b/ros2_deployment/compass_navigator/compass_navigator/obj_target_generator.py index ff26db5..5be71c0 100644 --- a/ros2_deployment/compass_navigator/compass_navigator/obj_target_generator.py +++ b/ros2_deployment/compass_navigator/compass_navigator/obj_target_generator.py @@ -15,7 +15,6 @@ # See the License for the specific language governing permissions and # limitations under the License. - # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # diff --git a/ros2_deployment/compass_navigator/setup.py b/ros2_deployment/compass_navigator/setup.py index 631ada7..0acfc34 100644 --- a/ros2_deployment/compass_navigator/setup.py +++ b/ros2_deployment/compass_navigator/setup.py @@ -13,20 +13,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - from glob import glob from setuptools import setup diff --git a/run.py b/run.py index 1c841ab..ee015fe 100644 --- a/run.py +++ b/run.py @@ -55,7 +55,7 @@ parser.add_argument('--wandb-project-name', '-n', type=str, - default='afm_rl_enhance', + required=True, help='The project name of W&B.') parser.add_argument('--wandb-run-name', '-r', @@ -85,6 +85,13 @@ parser.add_argument('--environment', type=str, help='Environment type') parser.add_argument('--num_envs', type=int, help='Number of environments') +# Multi-GPU training. Pair with `torchrun --nproc_per_node N run.py --distributed ...`; +# AppLauncher consumes this to bind each rank to its own GPU. +parser.add_argument('--distributed', + action='store_true', + default=False, + help='Run training across multiple GPUs (one process per GPU via torchrun).') + # Append AppLauncher cli args AppLauncher.add_app_launcher_args(parser) @@ -97,6 +104,7 @@ import gin import torch +import torch.distributed as dist import wandb from mobility_es.config import environments @@ -112,6 +120,27 @@ from compass.residual_rl.residual_ppo_trainer import ResidualPPOTrainer from compass.utils.logger import Logger + +class _NoOpLogger: + """Discards everything. Used on non-rank-0 processes in multi-GPU runs so + only rank 0 produces TensorBoard / W&B / artifact writes.""" + + def log_dict(self, *args, **kwargs): + pass + + def log_video(self, *args, **kwargs): + pass + + def log_artifact(self, *args, **kwargs): + pass + + def log_config(self, *args, **kwargs): + pass + + def close(self): + pass + + # Map from the embedding type to the RL env config. EmbodimentEnvCfgMap = { 'h1': H1GoalReachingEnvCfg, @@ -156,18 +185,51 @@ def run(run_mode, seed, enable_curriculum=False): - # Setup logger. - logger = Logger(log_dir=args_cli.output_dir, - backend=args_cli.logger, - experiment_name=args_cli.wandb_run_name, - project_name=args_cli.wandb_project_name, - entity=args_cli.wandb_entity_name) - - device = 'cuda' if torch.cuda.is_available() else 'cpu' + # Multi-GPU distributed setup. With `--distributed`, AppLauncher (already invoked + # at module load) reads LOCAL_RANK / RANK / WORLD_SIZE from torchrun's env, sets + # physics/active GPU per rank, and limits CPU threads. We still need to call + # init_process_group ourselves before any cross-rank op (param broadcast in + # ResidualPPOTrainer.__init__, gradient all-reduce in PPO.update). + if args_cli.distributed: + local_rank = app_launcher.local_rank + global_rank = app_launcher.global_rank + # Pin PyTorch's current CUDA device to this rank's GPU BEFORE + # init_process_group / any object-collective. NCCL's object + # collectives (dist.all_gather_object in _save_episode_logs) + # serialize through tensors built on torch.cuda.current_device(). + # Without this call current_device() defaults to 0 on every rank + # and object-collective traffic routes through GPU 0 instead of + # the rank's GPU. (Tensor all-reduces are unaffected because their + # tensors carry an explicit device.) + torch.cuda.set_device(local_rank) + if not dist.is_initialized(): + dist.init_process_group(backend="nccl") + device = f"cuda:{local_rank}" + is_rank_zero = global_rank == 0 + else: + local_rank = 0 + global_rank = 0 + device = 'cuda' if torch.cuda.is_available() else 'cpu' + is_rank_zero = True + + # Setup logger. Only rank 0 writes TensorBoard / W&B / artifacts; other ranks get + # a no-op logger that discards everything. + if is_rank_zero: + logger = Logger(log_dir=args_cli.output_dir, + backend=args_cli.logger, + experiment_name=args_cli.wandb_run_name, + project_name=args_cli.wandb_project_name, + entity=args_cli.wandb_entity_name) + else: + logger = _NoOpLogger() - # Setup base policy. + # Setup base policy. Pin DataParallel to the rank's GPU when distributed; let it + # span all visible GPUs in the single-process / single-GPU path (the legacy default). base_policy = XMobilityBasePolicy(args_cli.base_policy_path) - base_policy = torch.nn.DataParallel(base_policy) + if args_cli.distributed: + base_policy = torch.nn.DataParallel(base_policy, device_ids=[local_rank]) + else: + base_policy = torch.nn.DataParallel(base_policy) base_policy.to(device) base_policy.eval() @@ -175,7 +237,11 @@ def run(run_mode, if args_cli.distillation_policy_path is not None: distillation_policy = ESDistillationPolicyWrapper(args_cli.distillation_policy_path, embodiment) - distillation_policy = torch.nn.DataParallel(distillation_policy) + if args_cli.distributed: + distillation_policy = torch.nn.DataParallel(distillation_policy, + device_ids=[local_rank]) + else: + distillation_policy = torch.nn.DataParallel(distillation_policy) distillation_policy.to(device) distillation_policy.eval() else: @@ -211,18 +277,29 @@ def run(run_mode, env_cfg.viewer.env_index = 0 env_cfg.viewer.eye = (-2.5, -0.5, 1.5) - # Setup seed - env_cfg.seed = seed + # Setup seed. Per-rank offset diversifies env initial conditions across GPUs so + # rollouts collected by each rank explore different states (matches Isaac Lab's + # rsl_rl reference pattern). + env_cfg.seed = seed + global_rank + + # Pin PhysX + Isaac Sim's render device to this rank's GPU. Without this every + # rank's env_cfg.sim.device defaults to cuda:0 and all 8 sims pile onto a single + # GPU (caught with a Vulkan OOM during material loading on the first attempt). + if args_cli.distributed: + env_cfg.sim.device = device # Disable rewards, termination and curriculum for eval. if run_mode == 'eval' or run_mode == 'record': env_cfg.rewards = None env_cfg.terminations = None env_cfg.curriculum = None - env = RLESEnvWrapper(cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None) + # Only rank 0 records video — non-rank-0 ranks would compete for the same files + # under output_dir/videos/ and produce duplicates. + record_video = args_cli.video and is_rank_zero + env = RLESEnvWrapper(cfg=env_cfg, render_mode="rgb_array" if record_video else None) # Setup video if enabled. - if args_cli.video: + if record_video: video_kwargs = { "video_folder": os.path.join(args_cli.output_dir, "videos"), diff --git a/scripts/generate_omap_from_usd.py b/scripts/generate_omap_from_usd.py new file mode 100755 index 0000000..ef3cddb --- /dev/null +++ b/scripts/generate_omap_from_usd.py @@ -0,0 +1,308 @@ +#!/usr/bin/env python3 +# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Generate a 2D occupancy map (PNG + ROS YAML) from a USD scene. + +Wraps Isaac Sim's ``isaacsim.asset.gen.omap.bindings._omap.Generator`` and emits +files compatible with ``OccupancyMapCollisionChecker``. + +Output convention matches the existing bundled maps (top-left origin): the YAML +``origin`` is the world-coordinate of the *top-left* of the image (i.e. y = +ymax), and image row 0 is at y = ymax. + +Run via the Isaac Lab Python wrapper: + + ${ISAACLAB_PATH}/isaaclab.sh -p scripts/generate_omap_from_usd.py \ + compass/rl_env/exts/mobility_es/mobility_es/usd/office/office.usd + +By default the PNG + YAML land at ``/omap/`` next to the USD, which is +where ``OccupancyMapCollisionChecker``'s sibling-fallback looks first. +""" + +import argparse +import sys +from pathlib import Path + +# Initialize Isaac Sim before importing pxr / omni.physx. +# Match the headless-boot pattern used by .claude/skills/compass/scripts/sage10k_to_usd.py. +from isaacsim import SimulationApp # pylint: disable=import-error + +_app = SimulationApp({"headless": True}) + +# pylint: disable=wrong-import-position +import numpy as np +import omni.kit.app +import omni.physx +import omni.timeline +import omni.usd +import PIL.Image + +# isaacsim.asset.gen.omap is not auto-loaded by the standalone Isaac Sim app +# config; enable it explicitly before importing the bindings. +_OMAP_EXT_NAME = "isaacsim.asset.gen.omap" +_ext_manager = omni.kit.app.get_app().get_extension_manager() +_ext_manager.set_extension_enabled_immediate(_OMAP_EXT_NAME, True) + +from isaacsim.asset.gen.omap.bindings import _omap # pylint: disable=ungrouped-imports +from pxr import Sdf, Usd, UsdGeom, UsdPhysics + +# Buffer values written by Generator.update_settings(...) below. +# Generator.get_buffer() returns these integer codes per cell. +OCCUPIED_VALUE = 4 +FREE_VALUE = 5 +UNKNOWN_VALUE = 6 + +# Grayscale pixel values written into the output PNG. ROS map_server expects +# a near-white free pixel, near-black occupied pixel, and a mid-gray unknown. +PNG_OCCUPIED = 0 +PNG_FREE = 254 +PNG_UNKNOWN = 205 + +# ROS thresholds (matching the existing maps and Isaac Sim defaults). +ROS_OCCUPIED_THRESH = 0.65 +ROS_FREE_THRESH = 0.196 + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=__doc__.split("\n\n", maxsplit=1)[0], + formatter_class=argparse.ArgumentDefaultsHelpFormatter, + ) + parser.add_argument("usd_path", type=str, help="Path to the input USD scene.") + parser.add_argument( + "-o", + "--out-dir", + type=str, + default=None, + help="Output directory. Default: /omap/ (next to the USD).", + ) + parser.add_argument( + "--map-name", + type=str, + default=None, + help="Stem for the output PNG and YAML. Default: .", + ) + parser.add_argument( + "--cell-size", + type=float, + default=0.05, + help="Grid cell size in meters (matches existing bundled maps).", + ) + parser.add_argument( + "--z-min", + type=float, + default=0.1, + help="Lower height (meters) of the slab used for 2D rasterization.", + ) + parser.add_argument( + "--z-max", + type=float, + default=0.62, + help="Upper height (meters) of the slab used for 2D rasterization.", + ) + parser.add_argument( + "--bounds", + type=float, + nargs=4, + metavar=("XMIN", "XMAX", "YMIN", "YMAX"), + default=None, + help="World-space (x,y) bounds in meters. Default: derived from USD bbox + 1m padding.", + ) + parser.add_argument( + "--padding", + type=float, + default=1.0, + help="Padding (meters) added around the auto-derived bbox. Ignored when --bounds is given.", + ) + return parser.parse_args() + + +def compute_xy_bounds_from_stage(stage, padding: float): + """Compute world-space (xmin, xmax, ymin, ymax) bounds from the stage's bbox. + + Mirrors the policy used by Isaac Sim's omap UI "Auto Bound" button + (isaacsim.asset.gen.omap.ui/.../extension.py around line 350): only + Tokens.default_ purpose, no extent-hint short-circuit, fresh cache. + Some USDs ship stale extent hints that mislead BBoxCache into returning + garbage bounds, so we don't trust them. + """ + bbox_cache = UsdGeom.BBoxCache( + Usd.TimeCode.Default(), + includedPurposes=[UsdGeom.Tokens.default_], + ) + bbox_cache.Clear() + # Prefer /World if it exists (USD assembly convention); fall back to root. + world_prim = stage.GetPrimAtPath("/World") + if not world_prim or not world_prim.IsValid(): + world_prim = stage.GetPseudoRoot() + world_bbox = bbox_cache.ComputeWorldBound(world_prim) + aligned = world_bbox.ComputeAlignedRange() + if aligned.IsEmpty(): + raise RuntimeError("USD bbox is empty; pass --bounds explicitly.") + minp = aligned.GetMin() + maxp = aligned.GetMax() + return ( + float(minp[0]) - padding, + float(maxp[0]) + padding, + float(minp[1]) - padding, + float(maxp[1]) + padding, + ) + + +def ensure_physics_scene(stage) -> None: + """The occupancy generator queries PhysX. Ensure a UsdPhysics.Scene exists.""" + physics_path = Sdf.Path("/World/physicsScene") + if not stage.GetPrimAtPath(physics_path): + UsdPhysics.Scene.Define(stage, physics_path) + + +def buffer_to_grayscale_png(buffer, dims) -> np.ndarray: + """Convert the Generator's flat int buffer into a (H, W) uint8 grayscale image. + + The buffer is row-major with width = dims[0], height = dims[1]. Free pixels + map to ``PNG_FREE`` (white), occupied to ``PNG_OCCUPIED`` (black), unknown + to ``PNG_UNKNOWN`` (gray). The image is then flipped vertically so row 0 of + the PNG corresponds to y = ymax (the *top* of the world) — matches the + top-left origin convention used by the existing bundled OMaps. + """ + arr = np.array(buffer, dtype=np.int32) + expected = dims[0] * dims[1] + if arr.size != expected: + raise RuntimeError( + f"Buffer size {arr.size} != dims[0]*dims[1] = {expected} ({dims[0]}x{dims[1]})") + grid = arr.reshape((dims[1], dims[0])) # (H, W) + img = np.full_like(grid, PNG_UNKNOWN, dtype=np.uint8) + img[grid == OCCUPIED_VALUE] = PNG_OCCUPIED + img[grid == FREE_VALUE] = PNG_FREE + # Generator's buffer row 0 is at y = ymin (bottom). Flip so PNG row 0 = ymax + # (top), matching the existing top-left origin convention. + return np.flipud(img) + + +def write_yaml(yaml_path: Path, image_filename: str, resolution: float, origin_xy): + """Write a ROS-compatible map_server YAML. + + ``origin`` is the world (x, y) of the *top-left* corner of the image, matching + the COMPASS bundled-map convention consumed by ``OccupancyMapCollisionChecker``. + """ + text = (f"image: {image_filename}\n" + f"resolution: {resolution}\n" + f"origin: [{origin_xy[0]:.6f}, {origin_xy[1]:.6f}, 0.0]\n" + f"negate: 0\n" + f"occupied_thresh: {ROS_OCCUPIED_THRESH}\n" + f"free_thresh: {ROS_FREE_THRESH}\n") + yaml_path.write_text(text) + + +def main() -> int: + args = parse_args() + + usd_path = Path(args.usd_path).resolve() + if not usd_path.exists(): + print(f"ERROR: USD not found: {usd_path}", file=sys.stderr) + return 2 + + out_dir = Path(args.out_dir) if args.out_dir else usd_path.parent / "omap" + out_dir = out_dir.resolve() + out_dir.mkdir(parents=True, exist_ok=True) + + map_name = args.map_name or usd_path.stem + png_path = out_dir / f"{map_name}.png" + yaml_path = out_dir / "occupancy_map.yaml" + + print(f"[generate_omap] Loading USD: {usd_path}") + ctx = omni.usd.get_context() + ctx.open_stage(str(usd_path)) + # Wait for all references / textures / MDL shaders to finish loading before + # we rasterize. The `get_stage_loading_status()` tuple is (assets_loaded, + # assets_loading, assets_to_load); we block until the third element drops + # to zero, capped at ~5 minutes so we don't hang forever on a broken USD. + # Without this guard, generate2d() can crash kit when it queries fabric + # for prims whose references are still resolving (mirrors the + # `tearDown`-side wait in isaacsim.asset.gen.omap's own tests). + max_load_ticks = 6000 + for tick in range(max_load_ticks): + omni.kit.app.get_app().update() + loaded, loading, to_load = ctx.get_stage_loading_status() + if to_load == 0 and loading == 0: + print(f"[generate_omap] Stage fully loaded after {tick + 1} ticks " + f"(loaded={loaded}, loading={loading}, to_load={to_load})") + break + else: + print(f"[generate_omap] WARNING: stage still loading after {max_load_ticks} ticks; " + "proceeding anyway — generate2d() may rasterize a partially loaded scene.") + + stage = ctx.get_stage() + ensure_physics_scene(stage) + for _ in range(5): + omni.kit.app.get_app().update() + + if args.bounds is not None: + xmin, xmax, ymin, ymax = args.bounds + else: + print(f"[generate_omap] Computing bounds from USD bbox (padding {args.padding} m)") + xmin, xmax, ymin, ymax = compute_xy_bounds_from_stage(stage, args.padding) + + print(f"[generate_omap] Bounds (m): x=[{xmin:.3f}, {xmax:.3f}] " + f"y=[{ymin:.3f}, {ymax:.3f}] slab z=[{args.z_min:.3f}, {args.z_max:.3f}]") + print(f"[generate_omap] Cell size: {args.cell_size} m") + + # Drive the generator. Origin is the slab's mid-z, which is what update_location does. + z_mid = 0.5 * (args.z_min + args.z_max) + timeline = omni.timeline.get_timeline_interface() + timeline.play() + omni.kit.app.get_app().update() + + physx = omni.physx.get_physx_interface() + stage_id = omni.usd.get_context().get_stage_id() + generator = _omap.Generator(physx, stage_id) + generator.update_settings(args.cell_size, OCCUPIED_VALUE, FREE_VALUE, UNKNOWN_VALUE) + generator.set_transform( + (0.0, 0.0, z_mid), + (xmin, ymin, args.z_min), + (xmax, ymax, args.z_max), + ) + + # The C++ generator needs at least one app-update tick before generate2d. + omni.kit.app.get_app().update() + print("[generate_omap] Running generate2d() ...") + generator.generate2d() + omni.kit.app.get_app().update() + + timeline.stop() + + buffer = generator.get_buffer() + dims = generator.get_dimensions() + print(f"[generate_omap] Output dims (W x H): {dims[0]} x {dims[1]} " + f"(buffer size {len(buffer)})") + + img = buffer_to_grayscale_png(buffer, dims) + # Save as RGBA to match the existing bundled PNGs — OccupancyMapCollisionChecker + # accesses img[:, :, 0] regardless of the grayscale value. + rgba = np.stack([img, img, img, np.full_like(img, 255)], axis=-1) + PIL.Image.fromarray(rgba).save(png_path) + # Top-left origin convention: write (xmin, ymax) so YAML's `origin[1]` is + # the world Y of image row 0 (the top of the map). + write_yaml(yaml_path, png_path.name, args.cell_size, (xmin, ymax)) + + print(f"[generate_omap] Wrote PNG : {png_path}") + print(f"[generate_omap] Wrote YAML: {yaml_path}") + return 0 + + +if __name__ == "__main__": + rc = main() + _app.close() + sys.exit(rc) diff --git a/scripts/hdf5_to_lerobot_episodic.py b/scripts/hdf5_to_lerobot_episodic.py index ec2b81c..2cc7eb4 100755 --- a/scripts/hdf5_to_lerobot_episodic.py +++ b/scripts/hdf5_to_lerobot_episodic.py @@ -14,7 +14,6 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. - """ HDF5 to LeRobot Data Format Converter for Episodic Data