Research issues being addressed include real-time visual serving, adaptive camera calibration and windowing, hybrid estimation, and hybrid nonlinear control. Based on the reliability of sensory information, a supervisory control system switches among a fixed set of nonlinear controllers, each designed for a particular task such as blocking or striking the puck.
Future research is aimed at learning through repetitive play.
This project investigates the modeling of the sensorimotor control systems in humans and robots. We use techniques from control theory and artificial intelligence to understand dynamics and control of human motion and the mechanisms by which humans learn sensorimotor control. At the same time, we use studies of human motion to aid the development of improved control techniques for mechanical systems. Future applications of our work will include more dextrous robots and more effective diagnostics and physical therapy approaches for disabled humans, as well as better balance training and fall-prevention programs for elderly and individuals with balance deficits. An area of increasing interest is modeling and control of hybrid mechanical systems, that is, locomotion and grasping devices that interact with the environment via contacts and collisions.