# Unitree RL Gym
**Source:** https://github.com/unitreerobotics/unitree_rl_gym
**Fetched:** 2026-02-13
**Type:** GitHub Repository README

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# 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.