Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot
  Sim2Real Transfer
Xinyang Gu Yen-Jen Wang Jianyu Chen
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/.