A Reinforcement-Learning(RL) Training Framework for Legged Manipulation Robots

中文文档

Go2Arm_Lab enables RL training for the Go2Arm robot:

• Base platform: Unitree Go2 quadruped
• Manipulator: Interbotix WidowX 250s robotic arm

Version compatibility
This repository currently depends on IsaacLab v2.2.0.
For IsaacLab v2.1.0, please use the v2.1.0 version of this repository. For IsaacLab v1.4.1, please use the v1.4.1 version of this repository.

Gazebo deployment
If you want to deploy your policy in Gazebo, please use: Go2Arm_sim2sim

IsaacLab Simulation (v2.2)	Gazebo Simulation

For more videos, please visit my Bilibili homepage.

1. Follow the official guide to install IsaacLab v2.1.0.
2. Clone this repository into the same directory as IsaacLab:

   git clone https://github.com/zzzJie-Robot/Go2Arm_Lab.git
   

3. Install the package using the Python interpreter that IsaacLab uses:

   python -m pip install -e source/Go2Arm_Lab
   

Run reinforcement-learning training in headless mode for higher efficiency:

# Activate IsaacLab environment
conda activate your_isaaclab_env

# Go to Go2Arm_Lab
cd /path/to/Go2Arm_Lab

# Launch training (headless)
python scripts/rsl_rl/train.py --task Isaac-Go2Arm-Flat --headless

Deploy a trained policy in a single environment:

# Activate IsaacLab environment
conda activate your_isaaclab_env

# Go to IsaacLab root
cd /path/to/Go2Arm_Lab

# Run inference
python scripts/rsl_rl/play.py --task Isaac-Go2Arm-Flat-Play --num_envs 1

The RL algorithm implementation in this project references the Deep-Whole-Body-Control project, for which we extend our sincere gratitude.