See Something, Say Something: Context-Criticality-Aware Mobile Robot Communication for Hazard Mitigations
arXiv:2603.28901v1 Announce Type: new Abstract: The proverb ``see something, say something'' captures a core responsibility of autonomous mobile robots in safety-critical situations: when they detect a hazard, they must communicate--and do so quickly. In emergency scenarios, delayed or miscalibrated responses directly increase the time to action and the risk of damage. We argue that a systematic context-sensitive assessment of the criticality level, time sensitivity, and feasibility of mitigation is necessary for AMRs to reduce time to action and respond effectively. This paper presents a framework in which VLM/LLM-based perception drives adaptive message generation, for example, a knife in a kitchen produces a calm acknowledgment; the same object in a corridor triggers an urgent coordinat
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Abstract:The proverb ``see something, say something'' captures a core responsibility of autonomous mobile robots in safety-critical situations: when they detect a hazard, they must communicate--and do so quickly. In emergency scenarios, delayed or miscalibrated responses directly increase the time to action and the risk of damage. We argue that a systematic context-sensitive assessment of the criticality level, time sensitivity, and feasibility of mitigation is necessary for AMRs to reduce time to action and respond effectively. This paper presents a framework in which VLM/LLM-based perception drives adaptive message generation, for example, a knife in a kitchen produces a calm acknowledgment; the same object in a corridor triggers an urgent coordinated alert. Validation in 60+ runs using a patrolling mobile robot not only empowers faster response, but also brings user trusts to 82% compared to fixed-priority baselines, validating that structured criticality assessment improves both response speed and mitigation effectiveness.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2603.28901 [cs.RO]
(or arXiv:2603.28901v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.28901
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aliasghar Arab [view email] [v1] Mon, 30 Mar 2026 18:28:05 UTC (1,963 KB)
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