See Something, Say Something: Context-Criticality-Aware Mobile Robot Communication for Hazard Mitigations

TL;DR

This paper introduces a context-sensitive communication framework for autonomous robots, enabling faster, more effective hazard responses by assessing criticality and tailoring messages accordingly.

Contribution

It presents a novel framework using perception-driven adaptive messaging that improves response times and user trust in safety-critical robot scenarios.

Findings

Faster response times with the new framework compared to baselines.

User trust increased to 82% with structured criticality assessment.

Validated in over 60 runs with a patrolling mobile robot.

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.