注意機構に基づく地図エンコーディングによる汎化可能な脚式ロコモーションの学習
Attention-Based Map Encoding for Learning Generalized Legged Locomotion
脚式ロボットの強化学習制御において、ロボットの固有感覚に条件付けられた注意機構ベースの地図エンコーディングを提案し、多様な地形での汎化と疎な足場での精密な歩行を実現した。
詳しい要約
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著者: Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz BÄcher, Marco Hutter
分類: cs.RO
原文アブストラクト
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack precision on terrains with sparse steppable areas. Hybrid methods achieve enhanced robustness on sparse terrains by combining both methods but are computationally demanding and constrained by the inherent limitations of model-based planners. To achieve generalized legged locomotion on diverse terrains while preserving the robustness of learning-based controllers, this paper proposes to learn an attention-based map encoding conditioned on robot proprioception, which is trained as part of the end-to-end controller using reinforcement learning. We show that the network learns to focus on steppable areas for future footholds when the robot dynamically navigates diverse and challenging terrains. We synthesize behaviors that exhibit robustness against uncertainties while enabling precise and agile traversal of sparse terrains. Additionally, our method offers a way to interpret the topographical perception of a neural network. We have trained two controllers for a 12-DoF quadrupedal robot and a 23-DoF humanoid robot respectively and tested the resulting controllers in the real world under various challenging indoor and outdoor scenarios, including ones unseen during training.
