title: Learning an Internal Dynamics Model from Control Demonstration.
abstract: Much work in optimal control and inverse control has assumed that the controller has perfect knowledge of plant dynamics. However, if the controller is a human or animal subject, the subject's internal dynamics model may differ from the true plant dynamics. Here, we consider the problem of learning the subject's internal model from demonstrations of control and knowledge of task goals. Due to sensory feedback delay, the subject uses an internal model to generate an internal prediction of the current plant state, which may differ from the actual plant state. We develop a probabilistic framework and exact EM algorithm to jointly estimate the internal model, internal state trajectories, and feedback delay. We applied this framework to demonstrations by a nonhuman primate of brain-machine interface (BMI) control. We discovered that the subject's internal model deviated from the true BMI plant dynamics and provided significantly better explanation of the recorded neural control signals than did the true plant dynamics.
authors.affiliation: Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213 USA.
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Golub
authors.affiliation: Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213 USA.
authors.forename: Steven M
authors.initials: SM
authors.lastname: Chase
authors.affiliation: Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA 15213 USA.
authors.forename: Byron M
authors.initials: BM
authors.lastname: Yu