Humanoid robots can, in principle, use their legs to go almost anywhere. Developing controllers capable of traversing diverse terrains, however, remains a considerable challenge. Classical controllers are hard to generalize broadly while the learning-based methods have primarily focused on gentle terrains. Here, we present a learning-based approach for blind humanoid locomotion capable of traversing challenging natural and man-made terrain. Our method uses a transformer model to predict the next action based on the history of proprioceptive observations and actions. The model is first pre-trained on a dataset of flat-ground trajectories with sequence modeling, and then fine-tuned on uneven terrain using reinforcement learning. We evaluate our model on a real humanoid robot across a variety of terrains, including rough, deformable, and sloped surfaces. The model demonstrates robust performance, in-context adaptation, and emergent terrain representations. In real-world case studies, our humanoid robot successfully traversed over 4 miles of hiking trails in Berkeley and climbed some of the steepest streets in San Francisco.

本研究解决了类人机器人在多样地形上行走的控制器开发难题。通过使用变换器模型，从先前的运动观察和动作中预测下一步行动，提出了一种盲行走的学习方法，并在真实机器人上进行评估。实验结果表明，该模型在复杂地形上表现出卓越的适应能力和强大的应对能力，成功完成了在不同地形上的长距离行走。 

在困难地形上学习类人走路