  This paper presents an inverse optimal control methodology and its
application to training a predictive model of human motor control from a
manipulation task. It introduces a convex formulation for learning both
objective function and constraints of an infinite-horizon constrained optimal
control problem with nonlinear system dynamics. The inverse approach utilizes
Bellman's principle of optimality to formulate the infinite-horizon optimal
control problem as a shortest path problem and Lagrange multipliers to identify
constraints. We highlight the key benefit of using the shortest path
formulation, i.e., the possibility of training the predictive model with short
and selected trajectory segments. The method is applied to training a
predictive model of movements of a human subject from a manipulation task. The
study indicates that individual human movements can be predicted with low error
using an infinite-horizon optimal control problem with constraints on shoulder
movement.
