Distributed learning and control for networked robots that must coordinate, adapt, and remain safe as a team.
AIAS LabPurdue University Northwest
This research studies distributed learning and control for teams of autonomous robots that must coordinate under communication limits, uncertainty, and changing tasks. Graph-based representations and decentralized decision-making are used to capture interactions among agents.
Topics include graph neural networks, distributed reinforcement learning, formation control, cooperative manipulation, and scalable coordination for heterogeneous robot teams.