让机器人根据环境自动判断危险程度，快速准确报警。
See Something, Say Something: Context-Criticality-Aware Mobile Robot Communication for Hazard Mitigations
- 基于视觉大模型与语言模型感知，动态生成通信内容。
- 在60+次测试中将响应速度提升至82%用户信任度。
- 适合需要快速响应的安防、救援等高危场景应用。
俗话说‘发现异常，立即报告’，这正是自主移动机器人在安全关键场景中的核心职责：一旦检测到危险，必须迅速沟通。在紧急情况下，延迟或误判会直接延长响应时间，增加损害风险。本文提出一种框架，通过视觉-语言模型（VLM/LLM）感知实现对危险严重性、时间敏感性和可缓解性的系统性上下文评估，从而缩短响应时间并有效应对。例如，厨房中的刀具仅触发平静确认；同一物体出现在走廊则触发紧急协同警报。在60余次巡逻机器人实测中，该方法不仅显著加快响应，更使用户信任度达82%，高于固定优先级基线，验证了结构化临界性评估在提升响应速度与处置有效性方面的价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。