{"ID":2873484,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06768","arxiv_id":"2509.06768","title":"Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots","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.","short_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 r...","url_abs":"https://arxiv.org/abs/2509.06768","url_pdf":"https://arxiv.org/pdf/2509.06768v1","authors":"[\"Oluwadamilola Sotomi\",\"Devika Kodi\",\"Kiruthiga Chandra Shekar\",\"Aliasghar Arab\"]","published":"2025-09-08T14:53:19Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Language Model\"]","has_code":false}