フォールトトレラントな脚式移動のためのMixture-of-Experts強化学習
Mixture-of-Experts RL for Fault-Tolerant Legged Locomotion
脚式ロボットのアクチュエータ故障に対応するため、故障診断情報を利用して専門化された制御エキスパートを活性化するモジュール型アーキテクチャを提案し、単一ポリシーよりも高い性能を達成した。
詳しい要約
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著者: Giulio Turrisi, Ozan Pali, Luca Oneto, Claudio Semini
分類: cs.RO
原文アブストラクト
Legged robots deployed in planetary exploration and other remote environments must maintain reliable locomotion despite actuator failures and challenging terrain conditions. Although reinforcement learning has achieved strong results in legged locomotion, monolithic policies can struggle to efficiently represent the diverse control strategies required to compensate for different fault conditions. In this work, we propose a fault-aware modular control architecture that explicitly leverages fault-diagnosis information to activate specialized control experts associated with distinct actuator failure modes. Experimental results show that explicit fault-conditioned modular policies consistently outperform monolithic policies of comparable size, achieving higher locomotion performance across failure scenarios. Moreover, the proposed modular architecture retains competitive performance even under significantly reduced network capacity, highlighting its suitability for compute-constrained robotic platforms, such as those typically employed in space applications. The code associated with this work is available at: https://github.com/iit-DLSLab/fault-locomotion-isaaclab.
