Failing Gracefully: Mitigating Impact of Inevitable Robot Failures
arXiv:2608.05313v1 Announce Type: new Abstract: 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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[Submitted on 5 Aug 2026]
Title:Failing Gracefully: Mitigating Impact of Inevitable Robot Failures
View a PDF of the paper titled Failing Gracefully: Mitigating Impact of Inevitable Robot Failures, by Duc M. Nguyen and 5 other authors
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Abstract: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.
Comments: Accepted to IEEE International Conference on Robotics and Automation (ICRA 2026)
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
Cite as: arXiv:2608.05313 [cs.RO]
(or arXiv:2608.05313v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.05313
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Duc Nguyen [view email] [v1] Wed, 5 Aug 2026 18:15:15 UTC (1,536 KB)
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