Constrained Inverse Optimal Control with Application to a Human Manipulation Task
For researchers in human motor control and robotics, this work provides a principled way to infer constraints and cost functions from human demonstrations, but the application is limited to a specific manipulation task.
This paper proposes a convex inverse optimal control method to learn both the objective function and constraints of an infinite-horizon constrained optimal control problem, applied to model human motor control in a manipulation task. The method achieves low prediction error for individual human movements using short trajectory segments.
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.