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This is a repository for reinforcement learning implementation based on Unitree Go2. Official implementation of RSS 2026.
| Date | Stars |
|---|---|
| 2026-07-31 | 265 |
| 2026-08-01 | 266 |
| 2026-08-02 | 266 |
| 2026-08-05 | 267 |
| 2026-08-06 | 267 |
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<div align="center">
<h1 align="center">Go2 RL GYM</h1>
<p align="center">
<span>🌎 English</span> | <a href="README_zh.md">🇨🇳 中文</a> | <a href="https://arxiv.org/abs/2602.00678">📄 Paper [RSS 2026]</a>
</p>
</div>
<p align="center">
<strong>This repository builds on <a href="https://github.com/unitreerobotics/unitree_rl_gym">unitree_rl_gym</a> to train the Unitree Go2 quadruped with reinforcement learning.</br>For the IsaacLab-based version, see <a href="https://github.com/wertyuilife2/go2_rl_robotlab">go2_rl_robotlab</a>.</strong>
</p>
<div align="center">
| <div align="center"> Isaac Gym </div> | <div align="center"> Mujoco </div> | <div align="center"> Physical </div> |
|--- | --- | --- |
|  |  |  |
</div>
## 📦 Installation
Follow the step-by-step setup guide in [setup.md](doc/setup_en.md).
## 🛠️ Usage Guide
### 1. Train
Run the following command to launch training:
```bash
python legged_gym/scripts/train.py --task=xxx --headless
```
#### ⚙️ Arguments
- `--task`: Required. Options include `go2`, `go2_cts`, `go2_moe_cts`, `go2_moe_ng_cts`, `go2_mcp_cts`, `go2_ac_moe_cts`, `go2_dual_moe_cts`; `go2_moe_cts` is the paper's final version.
- `--headless`: Render viewer by default; set to `true` to disable rendering for higher throughput.
- `--resume`: Resume training from a chosen checkpoint in the logs.
- `--experiment_name`: Experiment folder to save/load from.
- `--run_name`: Run subfolder name to save/load from.
- `--load_run`: Name of the run to load (defaults to the most recent run).
- `--checkpoint`: Checkpoint index to load (defaults to the latest file).
- `--num_envs`: Number of parallel simulated environments.
- `--seed`: Random seed.
- `--max_iterations`: Maximum training iterations.
- `--sim_device`: Physics simulation device. Use `--sim_device=cpu` to force CPU.
- `--rl_device`: RL computation device. Use `--rl_device=cpu` to force CPU.
- `--robogauge`: Enable RoboGauge evaluation tool; disabled by default. Evaluation results are saved as `results_{it}.yaml` in `logs/{exp_name}/{date}/robogauge_results` and logged to TensorBoard.
- `--robogauge_port`: RoboGauge server port; default is 9973.
> RoboGauge evaluation requires a separate server to be started. Refer to the [RoboGauge documentation](https://github.com/wty-yy/RoboGauge).
**Default checkpoint path**: `logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt`
---
#### Model Evaluation
The trained model above was evaluated using the [RoboGauge](https://github.com/wty-yy/RoboGauge) framework via Sim2Sim. The models in the table below are the best models after 150k training steps.
All released checkpoints are hosted on Hugging Face: [wty-yy/go2_rl_gym_data](https://huggingface.co/wty-yy/go2_rl_gym_data).
| Model | Score | Tracking | Safety | Quality | Level | Download |
| --- | --- | --- | --- | --- | --- | --- |
| go2_moe_cts (Ours) | **0.6713** | **0.6669** | **0.7857** | **0.7392** | **7.85** | [ckpt](https://huggingface.co/wty-yy/go2_rl_gym_data/tree/main/go2_moe_cts_137000_0.6713) |
| go2_ac_moe_cts | 0.6509 | 0.6442 | 0.7644 | 0.7149 | 7.52 | [ckpt](https://huggingface.co/wty-yy/go2_rl_gym_data/blob/main/go2_ac_moe_cts_115k_0.6509.pt) |
| go2_mcp_cts | 0.6399 | 0.6355 | 0.7542 | 0.7058 | 7.41 | [ckpt](https://huggingface.co/wty-yy/go2_rl_gym_data/tree/main/go2_mcp_cts_91k_0.6399) |
| go2_moe_ng_cts | 0.6519 | 0.6447 | 0.7639 | 0.7186 | 7.56 | [ckpt](https://huggingface.co/wty-yy/go2_rl_gym_data/tree/main/go2_moe_ng_cts_79k_0.6519) |
| [CTS](https://arxiv.org/pdf/2405.10830) vanilla | 0.5786 | 0.5755 | 0.7066 | 0.6624 | 6.83 | [ckpt](https://huggingface.co/wty-yy/go2_rl_gym_data/Excerpt of 10,235 characters
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