Paper Info
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.
Year
Venue
Keywords
2013
ICML
bioinformatics,biomedical research
Field
DocType
ISSN
Neural control,Control theory,Optimal control,Animal subject,Expectation–maximization algorithm,Computer science,Inverse control,Artificial intelligence,Machine learning,Internal model,Probabilistic framework
Conference
1938-7288
Citations 
PageRank 
References 
7
0.53
9
Authors
3
Name
Order
Citations
PageRank
Matthew Golub1263.40
Steven M Chase2667.70
Byron M. Yu311513.65