優雅に失敗する：避けられないロボット故障の影響を軽減する
Failing Gracefully: Mitigating Impact of Inevitable Robot Failures
家庭環境でのロボット故障の影響を確率と重大性の両面から評価する安全策定法と、多様な故障モードをシミュレートするMuJoCoベースの評価フレームワークFailBenchを提案する。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Duc M. Nguyen, Saad A. Ghani, Andrew Marshall, Allison Andreyev, Gregory J. Stein, Xuesu Xiao
分類: cs.RO, cs.AI, cs.HC, cs.LG
原文アブストラクト
Service robots operate in household environments shared with humans, pets, and everyday objects, where they are highly susceptible to failures such as software crashes, hardware degradation, or unpredictable interactions. While roboticists strive to minimize failures, some remain inevitable, making it critical to mitigate their potential consequences for safe and reliable deployment. This paper introduces a novel safety formulation that evaluates both the probability of impactful interactions between robots and surrounding entities during failures, and the severity of their outcomes. By quantifying the impact of failures on different entities, our approach enables robots to make informed planning decisions that balance safety with task efficiency. To support systematic evaluation, we also present FailBench, a MuJoCo-based simulation framework for studying robot-environment interactions under diverse failure modes, including sensing issues and actuator malfunctions. Together, our safety formulation and FailBench provide a foundation for developing safer and more robust motion plans and learned policies in real-world household environments.