Aafiya Shamshad Hussain, Gaurav Srivastava, Alvi Md Ishmam, Zaber Ibn Abdul Hakim, Chris Thomas. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Videos from fleets of ground robots can advance public safety by providing scalable situational awareness and reducing professionals’ burden. Yet little is known about how to design and integrate multi-robot videos into public safety workflows. Collaborating with six police agencies, we examined how such videos could be made practical. In Study 1, we present the first testbed for multi-robot ground video sensemaking. The testbed includes 38 events of interest relevant to public safety, a dataset of 20 robot patrol videos (10 day/night pairs) covering EoI types, and 6 design requirements aimed at improving current video sensemaking practices. In Study 2, we built MRVS, a tool that augments multi-robot patrol video streams with a prompt-engineered video understanding model. Participants reported reduced manual workload and greater confidence with LLM-based explanations, while noting concerns about false alarms and privacy. We conclude with implications for designing future multi-robot video sensemaking tools.