Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer
This work addresses the challenge of sim-to-real transfer for humanoid robot locomotion, which is critical for robotics applications, though it appears incremental as it builds on existing frameworks like Nvidia Isaac Gym.
The authors tackled the problem of training locomotion skills for humanoid robots by developing Humanoid-Gym, a reinforcement learning framework that enables zero-shot transfer from simulation to real-world environments, achieving successful deployment on two humanoid robots (XBot-S and XBot-L) without additional real-world training.
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 framework is verified by RobotEra's XBot-S (1.2-meter tall humanoid robot) and XBot-L (1.65-meter tall humanoid robot) in a real-world environment with zero-shot sim-to-real transfer. The project website and source code can be found at: https://sites.google.com/view/humanoid-gym/.