ノイズ除去世界モデル学習によるヒューマノイドの難地形歩行の実現
Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning
ノイズ除去世界モデル学習(DWL)という強化学習フレームワークを提案し、ヒューマノイドが雪上や傾斜地、階段、極度の凹凸地形をゼロショットのsim-to-real転移で歩行できることを示した。
著者: Xinyang Gu, Yen-Jen Wang, Xiang Zhu, Chengming Shi, Yanjiang Guo, Yichen Liu, Jianyu Chen
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
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.
