发表机构
George Mason University; RobotiXX Lab（乔治梅森大学; RobotiXX实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对家庭服务机器人不可避免的故障，本文提出一种评估故障影响概率与严重程度的安全公式，结合FailBench模拟框架，为开发更安全的机器人运动规划及学习策略提供基础。
AI 中文摘要
服务机器人在与人类、宠物及日常物品共存的家庭环境中运行，极易出现软件崩溃、硬件退化或不可预测交互等故障。尽管机器人研究者正努力减少故障，但部分故障仍不可避免，因此缓解其对安全可靠部署的潜在后果至关重要。本文提出一种新颖的安全公式，既评估故障期间机器人与周围实体间产生影响的交互概率，又评估其结果的严重程度。通过量化故障对不同实体的影响，该方法使机器人能做出知情的规划决策，在安全与任务效率间取得平衡。为支持系统评估，本文还推出FailBench，一种基于MuJoCo的模拟框架，用于研究不同故障模式下的机器人-环境交互，包括感知问题和执行器故障。本文的安全公式与FailBench共同为在现实家庭环境中开发更安全、更鲁棒的运动规划及学习策略提供了基础。
英文摘要
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.
CommentsAccepted to IEEE International Conference on Robotics and Automation (ICRA 2026)
