Reinforcement Learning with the SO-ARM100 / SO-ARM101 in Isaac Lab

This repository implements tasks for the SO‑ARM100 and SO‑ARM101 robots using Isaac Lab. It serves as the foundation for several tutorials in the LycheeAI Hub series Project: SO‑ARM101 × Isaac Sim × Isaac Lab.

📰 News featuring this repository:

• Nov. 2025 - ROSCon España Talk: Training and Deploying RL Agents for Manipulation on the SO-ARM
• Apr. 2025 - NVIDIA Omniverse Livestream: Training a Robot from Scratch in Simulation (URDF → OpenUSD). Watch on YouTube
• Apr. 2025 - LycheeAI Tutorial: How to Create External Projects in Isaac Lab. Watch on YouTube

Installation

Install uv.

curl -LsSf https://astral.sh/uv/install.sh \| sh

Clone the repository.

git clone https://github.com/MuammerBay/isaac_so_arm101.git
cd isaac_so_arm101
uv sync

Quickstart

List available environments.

uv run list_envs

Test with dummy agents.

uv run zero_agent --task SO-ARM100-Reach-Play-v0    # send zero actions
uv run random_agent --task SO-ARM100-Reach-Play-v0  # send random actions

Reaching

Train a RL-based IK policy.

uv run train --task SO-ARM100-Reach-v0 --headless

Evaluate a trained policy.

uv run play --task SO-ARM100-Reach-Play-v0

Sim2Real Transfer

Work in progress.

Results

Acknowledgements

This project builds upon the excellent work of several open-source projects and communities:

• Isaac Lab — The foundational robotics simulation framework that powers this project
• NVIDIA Isaac Sim — The underlying physics simulation platform
• RSL-RL — Reinforcement learning library used for training policies
• SO-ARM100/SO-ARM101 Robot — The hardware platform that inspired this simulation environment
• WowRobo — Project sponsor providing assembled SO-ARM kits and parts (use code LYCHEEAI5 for 5% off)

Special thanks to the Isaac Lab development team at NVIDIA, Hugging Face and The Robot Studio for the SO‑ARM robot series, and the LycheeAI Hub community for tutorials and support.

Citation

If you use this work, please cite it as:

@software{Louis_Isaac_Lab_2025,
   author = {Louis, Le Lay and Muammer, Bay},
   doi = {https://doi.org/10.5281/zenodo.16794229},
   license = {BSD-3-Clause},
   month = apr,
   title = {Isaac Lab – SO‑ARM100 / SO‑ARM101 Project},
   url = {https://github.com/MuammerBay/isaac_so_arm101},
   version = {1.1.0},
   year = {2025}
}

License

See LICENSE for details.