Added module docs

docs/modules.md

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+# Dimensional Modules
+
+The DimOS Module system enables distributed, multiprocess robotics applications using Dask for compute distribution and LCM (Lightweight Communications and Marshalling) for high-performance IPC.
+
+## Core Concepts
+
+### 1. Module Definition
+Modules are Python classes that inherit from `dimos.core.Module` and define inputs, outputs, and RPC methods:
+
+```python
+from dimos.core import Module, In, Out, rpc
+from dimos.msgs.geometry_msgs import Vector3
+
+class MyModule(Module):
+    # Declare inputs/outputs as class attributes initialized to None
+    data_in: In[Vector3] = None  
+    data_out: Out[Vector3] = None
+
+    def __init__():
+        # Call parent Module init
+        super().__init__()
+
+    @rpc
+    def remote_method(self, param):
+        """Methods decorated with @rpc can be called remotely"""
+        return param * 2
+```
+
+### 2. Module Deployment
+Modules are deployed across Dask workers using the `dimos.deploy()` method:
+
+```python
+from dimos import core
+
+# Start Dask cluster with N workers
+dimos = core.start(4)
+
+# Deploying modules allows for passing initialization parameters.
+# In this case param1 and param2 are passed into Module init
+module = dimos.deploy(Module, param1="value1", param2=123)
+```
+
+### 3. Stream Connections
+Modules communicate via reactive streams using LCM transport:
+
+```python
+# Configure LCM transport for outputs
+module1.data_out.transport = core.LCMTransport("/topic_name", MessageType)
+
+# Connect module inputs to outputs
+module2.data_in.connect(module1.data_out)
+
+# Access the underlying Observable stream
+stream = module1.data_out.observable()
+stream.subscribe(lambda msg: print(f"Received: {msg}"))
+```
+
+### 4. Module Lifecycle
+```python
+# Start modules to begin processing
+module.start()  # Calls the @rpc start() method if defined
+
+# Inspect module I/O configuration  
+print(module.io().result())  # Shows inputs, outputs, and RPC methods
+
+# Clean shutdown
+dimos.shutdown()
+```
+
+## Real-World Example: Robot Control System
+
+```python
+# Connection module wraps robot hardware/simulation
+connection = dimos.deploy(ConnectionModule, ip=robot_ip)
+connection.lidar.transport = core.LCMTransport("/lidar", LidarMessage)
+connection.video.transport = core.LCMTransport("/video", Image)
+
+# Perception module processes sensor data
+perception = dimos.deploy(PersonTrackingStream, camera_intrinsics=[...])
+perception.video.connect(connection.video)
+perception.tracking_data.transport = core.pLCMTransport("/person_tracking")
+
+# Start processing
+connection.start()
+perception.start()
+
+# Enable tracking via RPC
+perception.enable_tracking()
+
+# Get latest tracking data
+data = perception.get_tracking_data()
+```
+
+## LCM Transport Configuration
+
+```python
+# Standard LCM transport for simple types like lidar
+connection.lidar.transport = core.LCMTransport("/lidar", LidarMessage)
+
+# Pickle-based transport for complex Python objects / dictionaries
+connection.tracking_data.transport = core.pLCMTransport("/person_tracking")
+
+# Auto-configure LCM system buffers (required in containers)
+from dimos.protocol import pubsub
+pubsub.lcm.autoconf()
+```
+
+This architecture enables building complex robotic systems as composable, distributed modules that communicate efficiently via streams and RPC, scaling from single machines to clusters.
+
+# Dimensional Install 
+## Python Installation (Ubuntu 22.04)
+
+```bash
+sudo apt install python3-venv
+
+# Clone the repository (dev branch, no submodules)
+git clone -b dev https://github.com/dimensionalOS/dimos.git
+cd dimos
+
+# Create and activate virtual environment
+python3 -m venv venv
+source venv/bin/activate
+
+sudo apt install portaudio19-dev python3-pyaudio
+
+# Install torch and torchvision if not already installed
+# Example CUDA 11.7, Pytorch 2.0.1 (replace with your required pytorch version if different)
+pip install torch==2.0.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
+```
+
+### Install dependencies
+```bash
+# CPU only (reccomended to attempt first)
+pip install .[cpu,dev]
+
+# CUDA install
+pip install .[cuda,dev]
+
+# Copy and configure environment variables
+cp default.env .env
+```
+
+### Test install 
+```bash 
+# Run standard tests
+pytest -s dimos/
+
+# Test modules functionality
+pytest -s -m module dimos/
+
+# Test LCM communication
+pytest -s -m lcm dimos/
+```
+
+# Unitree Go2 Quickstart
+
+To quickly test the modules system, you can run the Unitree Go2 multiprocess example directly:
+
+```bash
+# Make sure you have the required environment variables set
+export ROBOT_IP=<your_robot_ip>
+
+# Run the multiprocess Unitree Go2 example
+python dimos/robot/unitree_webrtc/multiprocess/unitree_go2.py
+```
+
+