Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer

Autor:	Gu, Xinyang, Wang, Yen-Jen, Chen, Jianyu
Rok vydání:	2024
Předmět:	Computer Science - Robotics Computer Science - Artificial Intelligence Computer Science - Machine Learning Electrical Engineering and Systems Science - Systems and Control
Zdroj:	ICRA 2024 Workshop on Agile Robotics
Druh dokumentu:	Working Paper
Popis:	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/.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2404.05695 Zobrazit plný text záznamu View this record from Arxiv