How to Set Up OpenClaw: A Practical Guide

TL;DR

• OpenClaw is an open-source RL framework for the Claw robotic hand, optimized for grasping and in-hand manipulation tasks.
• Requires: Python 3.8–3.10, MuJoCo (or PyBullet), and PyTorch/TensorFlow.
• Installation: Clone repo → pip install -e . → Set up MuJoCo.
• Best for: Researchers working with the Claw hand; alternatives like RoboSuite may be better for general [robotics](https://hyperion-<a href="/services/coaching-vs-consulting">consulting</a>.io/services/physical-ai).
• Gotchas: No active maintenance; MuJoCo license required.

## 1. Project Overview and Goals

OpenClaw is a reinforcement learning (RL) framework designed for the Claw robotic hand, a low-cost, open-source manipulator. It bridges the gap between <a href="/services/digital-twin-consulting">simulation</a> and real-world deployment for dexterous manipulation tasks like:

• Grasping (e.g., picking up objects of varying shapes).
• In-hand manipulation (e.g., rotating a cube).
• Tool use (e.g., using a wrench or screwdriver).

Key Goals:

1. Modular RL: Supports PPO, SAC, TD3, and DDPG out of the box OpenClaw GitHub.
2. Simulation-to-Real Transfer: Domain randomization tools to improve real-world performance OpenClaw Paper.
3. ROS/ROS2 Integration: Deploy trained policies to real hardware.
4. Benchmark Environments: Pre-configured tasks for reproducible research.

<a href="/services/physical-ai-robotics">physical ai</a> Stack Mapping:

• SENSE: Simulated/real sensor feedback (e.g., joint angles, tactile data).
• COMPUTE: RL policy training (PyTorch/TensorFlow).
• ACT: Low-level control of the Claw hand.
• ORCHESTRATE: ROS integration for multi-robot coordination.

## 2. Setting Up the Development Environment

Prerequisites

Step 1: Install Dependencies

# Install system dependencies (Ubuntu)
sudo apt update && sudo apt install -y \
    python3-pip \
    python3-venv \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libsm6 \
    libxrender1 \
    libxext6

# Create a virtual environment
python3 -m venv openclaw-env
source openclaw-env/bin/activate

Step 2: Install OpenClaw

git clone https://github.com/openclaw/openclaw.git
cd openclaw
pip install -e .

Expected Output:

Successfully installed openclaw-0.3.0

Step 3: Install MuJoCo

1. Download MuJoCo from mujoco.org.
2. Extract to ~/.mujoco/mujoco210 (or your preferred path).
3. Add to ~/.bashrc:

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco210/bin
   export MUJOCO_PY_MJKEY_PATH=~/.mujoco/mujoco210/bin/mjkey.txt
   

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4. Verify:

   python -c "import mujoco_py; print(mujoco_py.__version__)"
   

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Common Error:

• ImportError: libGL.so.1: cannot open shared object file Fix: Install missing libraries:

  sudo apt install libgl1-mesa-glx libglib2.0-0
  

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Step 4: Verify Installation

python -c "import openclaw; print(openclaw.__version__)"
# Output: 0.3.0

## 3. Core Components and Architecture

OpenClaw’s architecture follows a modular RL pipeline:

Example: Loading an Environment

import openclaw
from openclaw.envs import ClawGraspEnv

env = ClawGraspEnv(render=True)
obs = env.reset()
action = env.action_space.sample()
obs, reward, done, info = env.step(action)
env.close()

Expected Output:

• A MuJoCo window showing the Claw hand attempting to grasp an object.

## 4. Document Processing Pipeline (Simulation Data)

OpenClaw uses MuJoCo’s MJCF files to define the Claw hand and objects. Key files:

• openclaw/assets/claw.xml: Claw hand model.
• openclaw/envs/assets/: Object models (e.g., cubes, spheres).

Step 1: Modify an Environment

Edit openclaw/envs/claw_grasp_env.py to change the object:

def __init__(self):
    super().__init__()
    self.object_name = "cube"  # Change to "sphere" or "cylinder"

Step 2: Domain Randomization

Add a wrapper to randomize object properties:

from openclaw.wrappers import RandomizeDynamicsWrapper

env = ClawGraspEnv()
env = RandomizeDynamicsWrapper(
    env,
    mass_range=(0.1, 0.5),
    friction_range=(0.5, 1.0)
)

Gotcha:

• Randomization can destabilize training if ranges are too wide. Start with small variations.

## 5. Integration with LLMs (Optional)

While OpenClaw focuses on RL, you can integrate LLMs for high-level task planning (e.g., "pick up the red cube"). Example workflow:

1. Use an LLM to generate subgoals:

   from transformers import pipeline
   
   planner = pipeline("text-generation", model="gpt-4")
   task = "Pick up the red cube and place it in the box."
   subgoals = planner(task, max_length=50)
   

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2. Map subgoals to RL actions:

   if "pick up" in subgoals[0]:
       env.step(grasp_action)
   

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Alternative: Use LangChain to chain LLM prompts with OpenClaw policies.

## 6. Customization for EU Legal Frameworks (Not Applicable)

Note: OpenClaw is a robotics RL framework, not a legal tech tool. This section is intentionally omitted.

## 7. Contributing to the Project

Step 1: Fork and Clone

git clone https://github.com/your-username/openclaw.git
cd openclaw
git remote add upstream https://github.com/openclaw/openclaw.git

Step 2: Set Up Development Environment

pip install -e ".[dev]"
pre-commit install

Step 3: Run Tests

pytest tests/

Step 4: Submit a PR

1. Create a feature branch:

   git checkout -b feat/new-environment
   

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2. Commit changes:

   git commit -m "Add new environment for tool use"
   

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3. Push and open a PR on GitHub.

Gotcha:

• The project is in maintenance mode, so PRs may take time to review.

## Alternatives to OpenClaw

## What's Next?

1. Train a policy:

   python scripts/train.py --env ClawGraspEnv --algo PPO
   

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2. Deploy to real hardware:
   • Set up ROS/ROS2 and use ClawROSNode.
3. Extend with custom environments:
   • Modify openclaw/envs/ to add new tasks.

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