AcL: 関節故障に耐える四足歩行制御のための行動学習器
AcL: Action Learner for Fault-Tolerant Quadruped Locomotion Control
複数の関節が故障しても、教師方策のスタイル報酬を活用した生徒方策の蒸留により、四足ロボットが自動で跛行歩容に切り替えて安定歩行できる強化学習フレームワークを提案した。
著者: Tianyu Xu, Yaoyu Cheng, Pinxi Shen, Lin Zhao
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Quadrupedal robots can learn versatile locomotion skills but remain vulnerable when one or more joints lose power. In contrast, dogs and cats can adopt limping gaits when injured, demonstrating their remarkable ability to adapt to physical conditions. Inspired by such adaptability, this paper presents Action Learner (AcL), a novel teacher-student reinforcement learning framework that enables quadrupeds to autonomously adapt their gait for stable walking under multiple joint faults. Unlike conventional teacher-student approaches that enforce strict imitation, AcL leverages teacher policies to generate style rewards, guiding the student policy without requiring precise replication. We train multiple teacher policies, each corresponding to a different fault condition, and subsequently distill them into a single student policy with an encoder-decoder architecture. While prior works primarily address single-joint faults, AcL enables quadrupeds to walk with up to four faulty joints across one or two legs, autonomously switching between different limping gaits when faults occur. We validate AcL on a real Go2 quadruped robot under single- and double-joint faults, demonstrating fault-tolerant, stable walking, smooth gait transitions between normal and lamb gaits, and robustness against external disturbances.
