让机器人在复杂地形上自主行走，靠的是视觉盲控的Transformer模型。
Learning Humanoid Locomotion over Challenging Terrain
- 用Transformer根据身体感知历史预测下一步动作
- 预训练+强化学习微调，在崎岖地面实现稳定行走
- 适合做真实场景下复杂地形导航的机器人研究
类人机器人理论上可利用双腿前往几乎任何地方。然而，开发能穿越多样化地形的控制器仍是重大挑战。传统控制器泛化能力差，基于学习的方法多集中于平坦地形。本文提出一种无需视觉信息的类人机器人行走学习方法，可在自然和人工复杂地形上通行。该方法使用Transformer模型，基于本体感受观测与动作历史预测下一动作。模型先在平坦地面轨迹数据集上进行序列建模预训练，再通过强化学习在不平整地形上微调。我们在真实类人机器人上评估了该模型，覆盖粗糙、可变形及斜坡等多种地形。结果表明，模型具备鲁棒性能、上下文自适应能力以及涌现的地形表征能力。在实际案例中，该机器人成功穿越伯克利超过4英里山地小径，并攀爬旧金山部分最陡街道。
原文摘要 · Abstract (English)
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.
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