---
license: apache-2.0
tags:
  - reinforcement-learning
  - robotics
  - legged-locomotion
  - quadruped
  - mujoco
  - mjlab
  - rsl-rl
  - ppo
  - onnx
pipeline_tag: reinforcement-learning
---

# XGO — Luwu Dynamics Quadruped Locomotion Policies

PPO locomotion policies for the Luwu Dynamics (陆吾智能, [xgorobot.com](https://www.xgorobot.com/)) quadruped robots **Mini2S**, **Mini2SW** (wheeled variant), and **Lite3**, trained with [RSL-RL](https://github.com/leggedrobotics/rsl_rl) in [mjlab](https://github.com/mujocolab/mjlab) (Isaac-Lab-style APIs on GPU-accelerated MuJoCo Warp) via the [luwu_mjlab](https://github.com/LuwuDynamics/luwu_mjlab) training environment.

## Policies

| Folder | Task ID | Robot | Behavior | Iterations | Final mean reward |
|---|---|---|---|---|---|
| `mini2s_walk/` | `Mini2S-Walk-Flat` | Mini2S | Flat-terrain velocity-tracking walk | 1500 | 87.1 |
| `mini2s_turn/` | `Mini2S-Turn-Flat` | Mini2S | Fall recovery / turn-over | 1500 | 112.9 |
| `mini2sw_walk/` | `Mini2SW-Walk-Flat` | Mini2SW (wheeled) | Flat-terrain velocity-tracking walk | 1500 | 89.4 |
| `mini2sw_turn/` | `Mini2SW-Turn-Flat` | Mini2SW (wheeled) | Fall recovery / turn-over | 1500 | 97.6 |
| `lite3_walk/` | `Lite3-Walk-Flat` | Lite3 | Flat-terrain velocity-tracking walk | 1500 | 69.3 |

Each folder contains:
- `policy.onnx` — exported inference-only policy (observation → action), robot- and deployment-framework agnostic.
- `model_1499.pt` — final RSL-RL PPO checkpoint (actor-critic weights + optimizer state), for resuming training or loading in `luwu_mjlab`/`rsl_rl`.
- `params/env.yaml`, `params/agent.yaml` — the exact environment and PPO hyperparameters used for that run.

## Training setup

- Trained on a single NVIDIA GPU, 4096 parallel environments, 1500 PPO iterations per policy.
- Simulator: MuJoCo 3.6.0 / mujoco-warp 3.6.0 (GPU-accelerated).
- Framework: [mjlab](https://github.com/mujocolab/mjlab) v1.2.0 + [rsl-rl-lib](https://github.com/leggedrobotics/rsl_rl) 5.0.1.
- Task source: [luwu_mjlab](https://github.com/LuwuDynamics/luwu_mjlab) `src/tasks/velocity/`.

## Usage

With `luwu_mjlab` installed, evaluate a checkpoint:

```bash
python scripts/play.py Mini2S-Walk-Flat \
  --checkpoint-file /path/to/mini2s_walk/model_1499.pt
```

Or run the exported ONNX policy directly with `onnxruntime` for deployment (input/output tensor names and shapes are defined by `luwu_mjlab`'s observation/action manager for each task — see `params/env.yaml` in the matching folder).

## Disclaimer

These policies were trained in simulation only and have not been validated on hardware. Evaluate carefully (and add safety measures) before any real-robot deployment.