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arXiv · 2509.06768

Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots

Oluwadamilola Sotomi·Devika Kodi·Kiruthiga Chandra Shekar·Aliasghar Arab
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Abstract

Autonomous robots operating in dynamic environments should identify and report anomalies. Embodying proactive mitigation improves safety and operational continuity. This paper presents a multimodal anomaly detection and mitigation system that integrates vision-language models and large language models to identify and report hazardous situations and conflicts in real-time. The proposed system enables robots to perceive, interpret, report, and if possible respond to urban and environmental anomalies through proactive detection mechanisms and automated mitigation actions. A key contribution in this paper is the integration of Hazardous and Conflict states into the robot's decision-making framework, where each anomaly type can trigger specific mitigation strategies. User studies (n = 30) demonstrated the effectiveness of the system in anomaly detection with 91.2% prediction accuracy and relatively low latency response times using edge-ai architecture.

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Oluwadamilola Sotomi, Devika Kodi, Kiruthiga Chandra Shekar, Aliasghar Arab. 2025-09-08. Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots. https://arxiv.org/abs/2509.06768

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