让机器人在故障时也能安全应对，减少对人和环境的伤害。
Failing Gracefully: Mitigating Impact of Inevitable Robot Failures
- 用概率与后果双重评估故障影响，指导机器人安全决策
- 提出新框架可量化故障对不同实体的影响程度
- 适合研究家用机器人安全与鲁棒性设计的团队
服务机器人在人类、宠物和日常物品共存的家庭环境中运行，极易遭遇软件崩溃、硬件老化或不可预测交互等故障。尽管机器人研发者力求降低故障率，但部分故障仍不可避免，因此需减轻其潜在后果以保障安全可靠部署。本文提出一种新的安全评估方法，同时衡量故障期间机器人与周围实体发生重要交互的概率及其后果严重性。通过量化不同实体受故障影响的程度，该方法使机器人能在安全性与任务效率间做出更明智的规划决策。为支持系统化评估，我们还构建了FailBench——一个基于MuJoCo的仿真框架，用于研究多种故障模式下的机器人-环境交互，包括感知异常和执行器失效。安全评估方法与仿真平台相结合，为实现在真实家庭环境中开发更安全、更鲁棒的运动规划与学习策略提供了基础。
原文摘要 · Abstract (English)
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.
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