Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning
<|reference_start|>Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning: 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.<|reference_end|>
@article{gu2024advancing,
    title={Advancing Humanoid Locomotion: Mastering Challenging Terrains with
  Denoising World Model Learning},
    author={Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo,
  Yichen Liu, Jianyu Chen},
    journal={arXiv preprint arXiv:2408.14472},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14472},
    primaryClass={cs.RO cs.AI cs.SY eess.SY}
}