Welcome to the training project prepared for the Intelligent Robotics course, focusing on policy training through reinforcement learning for the Unitree H1 humanoid robot! This notebook is presented by the Networked Robotics Systems Laboratory (NRSL) and utilizes the powerful MuJoCo simulation environment developed by Google DeepMind.
The training is built upon the MuJoCo Playground and utilizes the MuJoCo XLA (MJX) — a JAX-based implementation of MuJoCo — enabling efficient reinforcement learning (RL) policy training within minutes using a single GPU.
🚀 Goal: Accelerate development and training of humanoid robots using state-of-the-art reinforcement learning techniques in a high-fidelity physics simulator.
You can quickly get started with training by running the notebook directly on Google Colab. No local installation required — just click and run!
| 💻 Colab | 📋 Description |
|---|---|
 |
Train the Unitree H1 humanoid robot using reinforcement learning |
Here are a few ways instructors can integrate this project into coursework:
- 🔧 Modify the reward function to improve stability
- 🏃♂️ Train for different locomotion goals (e.g., walking backward, sidestepping)
- 📈 Plot learning curves to analyze policy performance
- MuJoCo Official Website – Multibody physics engine
- MuJoCo Playground – Interactive demo environment
- MuJoCo XLA (MJX) – JAX-native MuJoCo for machine learning
- Unitree H1 Robot Overview – Hardware specifications and real-world applications