Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer
Xinyang Gu²*, Yen-Jen Wang¹³*, Jianyu Chen¹²³
¹ Shanghai Qi Zhi Institute ² Robot Era ³ Tsinghua University
∗Equal contribution. Listed alphabetically.
Paper: https://arxiv.org/abs/2404.05695
GitHub Repository: https://github.com/roboterax/humanoid-gym
Humanoid-Gym is an easy-to-use reinforcement learning (RL) framework based on Nvidia Isaac Gym, designed to train locomotion skills for humanoid robots, emphasizing zero-shot transfer from simulation to the real-world environment. Humanoid-Gym also integrates a sim-to-sim framework from Isaac Gym to Mujoco that allows users to verify the trained policies in different physical simulations to ensure the robustness and generalization of the policies.
This codebase is verified by RobotEra's XBot-S (1.2 meter tall humanoid robot) and XBot-L (1.65 meter tall humanoid robot) in real-world environment with zero-shot sim-to-real transfer.
Robot Hardware Platform for Evaluation
XBot-S
Tall: 1.2 meters
Weight: 38 kg
Actuated Motors: 26
Total DOF: 32
XBot-L
Tall: 1.65 meters
Weight: 57 kg
Actuated Motors: 54
Total DOF: 60
Performance Comparison in Different Environments
Nvidia Isaac Gym
Sim-to-sim (Mujoco)
Sim-to-real
Challenging Terrains With Zero-shot Sim-to-sim Transfer
Front View
Side View
Challenging Locomotion Task in Real-World Environments (Denoising World Model Learning)
Snowy Terrain
Dynamic Uneven Terrain
Dynamic Payload
(10 kg)
External Force
Go Up/Down Stairs
Push 60 kg Cart
Uneven Terrain
(Ankle View)
Go Up/Down Slopes
Dexterous Hand Manipulation (Coming Soon)
Stack Blocks
Prepare Food
Citation
@article{gu2024humanoid,
title={Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer},
author={Gu, Xinyang and Wang, Yen-Jen and Chen, Jianyu},
journal={arXiv preprint arXiv:2404.05695},
year={2024}
}
Acknowledgment
The implementation of Humanoid-Gym relies on resources from legged_gym and rsl_rl projects, created by the Robotic Systems Lab. We specifically utilize the LeggedRobot implementation from their research to enhance our codebase.
Any Questions?
If you have further questions, please feel free to contact support@robotera.com.