Multi-Robot Systems (MSL)
MSL develops algorithms for collaboration, coordination, and competition among machine/human teams in unstructured natural environments, including operation in complex traffic understanding and signaling intent, in 3D flying races against human pilots at the edge of the dynamic envelope; and for large-scale drone aerial remote surveys. Building on optimization, control and game theory, and machine learning, we develop the essentials for robots entering the real world.
Intelligent Systems Laboratory (SISL)
SISL studies robust decision-making in settings involving complex and dynamic environments where safety and efficiency must be balanced. We apply our work to challenges including autonomous driving, route planning, deep reinforcement learning, and safety and validation, addressing algorithms for efficiently deriving optimal decision strategies from high-dimensional, probabilistic representations, and establishing confidence in their safe and correct application in the real world.