# Constrained Inverse Optimal Control with Application to a Human Manipulation Task

## TL;DR

This paper introduces a convex inverse optimal control method that models human manipulation tasks by learning objectives and constraints, enabling accurate prediction of human movements from limited trajectory data.

## Contribution

It proposes a novel convex formulation for inverse optimal control that incorporates constraints and uses shortest path principles, applicable to nonlinear dynamics.

## Key findings

- Low-error prediction of human movements achieved
- Method effectively learns constraints from data
- Applicable to nonlinear system dynamics

## Abstract

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.