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This paper introduces a framework for context-aware communication in mobile robots, leveraging VLMs/LLMs to generate adaptive messages based on hazard criticality, time sensitivity, and mitigation feasibility. The system dynamically adjusts communication urgency based on contextual understanding of detected hazards, such as a knife being benign in a kitchen but dangerous in a corridor. Experiments with a patrolling robot show the approach improves response speed and increases user trust to 82% compared to fixed-priority communication strategies.
Context-aware robots that "see something, say something" boost user trust by 82% simply by communicating more intelligently about hazards.
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.
