Whole-body Humanoid Robot Locomotion with Human Reference
Qiang Zhang, Peter Cui, David Yan, Jingkai Sun, Yiqun Duan, Gang Han, Wen Zhao, Weining Zhang
Abstract
Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot, "Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot. For more video demonstrations, please visit our YouTube channel: https://www.youtube.com/watch?v=7hK2ySYBa1I
BibTeX
@inproceedings{iros2024_wholebodyhumanoi,
title = {Whole-body Humanoid Robot Locomotion with Human Reference},
author = {Qiang Zhang and Peter Cui and David Yan and Jingkai Sun and Yiqun Duan and Gang Han and Wen Zhao and Weining Zhang and Yijie Guo and Arthur Zhang and Renjing Xu},
booktitle = {IROS 2024},
year = {2024}
}