Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911142909050880 |
|---|---|
| author | Sotomi, Oluwadamilola Kodi, Devika Shekar, Kiruthiga Chandra Arab, Aliasghar |
| author_facet | Sotomi, Oluwadamilola Kodi, Devika Shekar, Kiruthiga Chandra Arab, Aliasghar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots Sotomi, Oluwadamilola Kodi, Devika Shekar, Kiruthiga Chandra Arab, Aliasghar Robotics 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. |
| title | Embodied Hazard Mitigation using Vision-Language Models for Autonomous Mobile Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.06768 |