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.

翻译：人形机器人原则上可以利用其腿部行走至几乎任何地方。然而，开发能够穿越多样化地形的控制器仍然是一个巨大的挑战。经典控制器难以广泛泛化，而基于学习的方法主要集中于平缓地形。在此，我们提出一种基于学习的盲人形机器人行走方法，能够穿越具有挑战性的自然与人造地形。我们的方法使用一个Transformer模型，基于本体感知观测与动作的历史序列来预测下一个动作。该模型首先通过序列建模在平地面轨迹数据集上进行预训练，然后利用强化学习在不平地形上进行微调。我们在真实人形机器人上评估了该模型，测试地形包括粗糙、可变形及倾斜表面。该模型展现出鲁棒的性能、上下文适应能力以及涌现的地形表征能力。在真实世界案例研究中，我们的人形机器人成功穿越了伯克利超过4英里的徒步小径，并攀爬了旧金山一些最陡峭的街道。