This research explores how heterogeneous AI agents can establish common ground during collaboration. By separating communication and action into distinct decision-making policies, agents can engage in micro-conversations that create shared understanding. The work aims to improve teamwork among diverse robots and support future human-AI collaboration in complex environments.

This research develops a distributed multi-robot task allocation framework that enables autonomous robots to estimate tasks, share information, coordinate assignments, and avoid collisions without relying on a central server. The approach improves efficiency, scalability, and resilience, with applications in emergency response, particularly supporting firefighters during life-saving operations.

Marine debris severely harms ocean ecosystems, yet most cleanup focuses only on floating waste. This research develops underwater robots equipped with specialized computer vision to detect and remove submerged trash. By training algorithms for challenging underwater conditions, the work enables safer, scalable cleanup operations and supports long-term ocean preservation.