Humanoid-Gym is an advanced reinforcement learning (RL) framework built on Nvidia Isaac Gym, designed for training locomotion skills in humanoid robots. Notably, it emphasizes zero-shot transfer, enabling skills learned in simulation to be directly applied to real-world environments without additional adjustments.
Humanoid-Gym streamlines the process of training humanoid robots by providing an intuitive and efficient platform. By integrating Nvidia Isaac Gym with MuJoCo, it allows users to test and verify trained policies in various simulation environments. This capability ensures the robustness and versatility of the trained behaviors, facilitating a seamless transition from virtual training to real-world application.