2.6 KiB
Unitree RL Gym
Source: https://github.com/unitreerobotics/unitree_rl_gym Fetched: 2026-02-13 Type: GitHub Repository README
Unitree RL GYM: Complete Project Overview
Project Description
Unitree RL GYM is "a repository for reinforcement learning implementation based on Unitree robots, supporting Unitree Go2, H1, H1_2, and G1." The system enables training robotic policies in simulation and deploying them to physical hardware through a structured pipeline.
Supported Robots
The framework supports four Unitree robot platforms:
- Go2: Quadruped robot
- G1: Humanoid robot
- H1: Humanoid robot
- H1_2: Enhanced humanoid variant
Training Workflow: Train -> Play -> Sim2Sim -> Sim2Real
1. Training Phase
Execute training using Isaac Gym simulation environment:
python legged_gym/scripts/train.py --task=xxx
Key parameters include:
--task: Robot selection (go2, g1, h1, h1_2)--headless: High-efficiency mode without visualization--num_envs: Parallel environment count--max_iterations: Training duration--sim_device/--rl_device: GPU/CPU specification
Training outputs save to: logs/<experiment_name>/<timestamp>_<run_name>/model_<iteration>.pt
2. Play/Validation Phase
Verify trained policies with visualization:
python legged_gym/scripts/play.py --task=xxx
The system exports actor networks as:
policy_1.pt(standard MLP networks)policy_lstm_1.pt(RNN-based networks)
3. Sim2Sim Deployment (Mujoco)
Cross-simulator validation ensures policies generalize beyond Isaac Gym:
python deploy/deploy_mujoco/deploy_mujoco.py {config_name}
Configuration files located in deploy/deploy_mujoco/configs/ enable model substitution via policy_path parameter.
4. Sim2Real Deployment (Physical Robots)
Deploy to actual hardware with prerequisite of "debug mode" activation:
python deploy/deploy_real/deploy_real.py {net_interface} {config_name}
Parameters specify network interface (e.g., enp3s0) and robot config files.
C++ Alternative: Pre-compiled G1 deployment available in deploy/deploy_real/cpp_g1/ using LibTorch library.
Technical Architecture
Language Composition:
- Python: 90.8%
- C++: 8.2%
- Other: 1.0%
Core Dependencies:
- legged_gym: Foundation framework
- rsl_rl: RL algorithm implementation
- Mujoco: Physics simulation
- unitree_sdk2_python: Hardware communication interface
- LibTorch: C++ neural network inference
License
BSD 3-Clause License governs usage, requiring copyright retention, prohibiting promotional misuse, and mandating modification disclosure.