We will utilize techniques form control theory and artificial intelligence to improve our understanding of the dynamics and control of human movement and the mechanisms by which humans learn sensorimotor control. Applications include more dextrous robots, more effective diagnostic and physical therapy approaches for disabled humans, and better balance training and falls prevention programs for the elderly and individuals with balance deficits. This project seeks to develop stability and tracking results for underactuated mechanical systems using tools from Lagrangian and Hamiltonian dynamics, geometric nonlinear control theory, hybrid control, and saturation. Research issues being addressed include real-time visual servoing, 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.