難地形を歩行するヒューマノイドの学習
Learning Humanoid Locomotion over Challenging Terrain
固有感覚と行動の履歴から次行動を予測するTransformerを平坦地で事前学習し強化学習で不整地に微調整することで、実機ヒューマノイドが未整備の自然地形や急斜面を安定して歩行できるようにした。
著者: Ilija Radosavovic, Sarthak Kamat, Trevor Darrell, Jitendra Malik
分類: cs.RO, cs.LG
原文アブストラクト
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.
