The Lab focuses on the design of optimization-based algorithms for robot control and coordination in human-inhabited environments. Our goal is to ensure a safe, reliable, secure, and private coordination of the robots among humans.
The main methods involved in our research are nonlinear model predictive control (MPC) and decomposition methods (e.g., ADMM and AMA). We also investigate control strategies using deep reinforcement learning. Finally, we are exploring how to integrate privacy-preserving algorithms in the the context of robot control and planning.
The main application domains of the Lab’s research are automotive, maritime transportation, manipulators, and UAVs.