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/.

翻译：Humanoid-Gym是一个基于Nvidia Isaac Gym的易用强化学习框架，专为训练人形机器人运动技能而设计，重点实现从仿真到真实环境的零样本迁移。该框架还集成了从Isaac Gym到Mujoco的仿真到仿真架构，使用户能够在不同物理仿真环境中验证训练策略，以确保策略的鲁棒性和泛化能力。本框架通过RobotEra的XBot-S（1.2米高的人形机器人）和XBot-L（1.65米高的人形机器人）在真实环境中进行了零样本仿真到现实迁移的验证。项目网站和源代码可访问：https://sites.google.com/view/humanoid-gym/。