関節故障に耐える脚式ロボットの統合型レジリエント制御
UMC: Unified Resilient Controller for Legged Robots with Joint Malfunctions
脚式ロボットの関節故障やセンサ故障に適応するため、マスキング機構を用いた2段階学習のモデルフリー制御フレームワークUMCを提案し、故障時のタスク達成率を平均36〜39%向上させた。
著者: Yu Qiu, Xin Lin, Jingbo Wang, Xiangtai Li, Lu Qi, Ming-Hsuan Yang
分類: cs.RO
原文アブストラクト
Adaptation to unpredictable damages is crucial for autonomous legged robots, yet existing methods based on multi-policy or meta-learning frameworks face challenges like limited generalization and complex maintenance. To address this issue, we first analyze and summarize eight types of damage scenarios, including sensor failures and joint malfunctions. Then, we propose a novel, model-free, two-stage training framework, Unified Malfunction Controller (UMC), incorporating a masking mechanism to enhance damage resilience. Specifically, the model is initially trained with normal environments to ensure robust performance under standard conditions. In the second stage, we use masks to prevent the legged robot from relying on malfunctioning limbs, enabling adaptive gait and movement adjustments upon malfunction. Experimental results demonstrate that our approach improves the task completion capability by an average of 36% for the transformer and 39% for the MLP across three locomotion tasks. The source code and trained models will be made available to the public.
