Robotics: Science and Systems XX

Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning

Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, Jianyu Chen

Abstract:

Humanoid robots, with their human-like skeletal structure, are especially suited for tasks in human-centric environments. However, this structure is accompanied by additional challenges in locomotion controller design, especially in complex real-world environments. As a result, existing humanoid robots are limited to relatively simple terrains, either with model-based control or model-free reinforcement learning. In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains. All scenarios run the same learned neural network with zero-shot sim-to-real transfer, indicating the superior robustness and generalization capability of the proposed method.

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Bibtex:

  
@INPROCEEDINGS{Gu-RSS-24, 
    AUTHOR    = {Xinyang Gu AND Yen-Jen Wang AND Xiang Zhu AND Chengming Shi AND Yanjiang Guo AND Yichen Liu AND Jianyu Chen}, 
    TITLE     = {{Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2024}, 
    ADDRESS   = {Delft, Netherlands}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2024.XX.058} 
}