Authors: 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.
Source: http://arxiv.org/abs/2408.14472v1
