Optimal control models of the goal-oriented human locomotion

TL;DR

This paper explores modeling human goal-oriented locomotion as an inverse optimal control problem, analyzing the system dynamics, cost functions, and the asymptotic behavior of optimal trajectories as targets extend infinitely.

Contribution

It introduces a framework for modeling human locomotion using inverse optimal control and analyzes the asymptotic behavior of optimal trajectories for large target distances.

Findings

Optimal trajectories tend to a specific asymptotic form as target distance increases.

The modeling approach provides insights into the underlying control strategies of human movement.

The paper discusses the implications for understanding and replicating human locomotion.

Abstract

In recent papers it has been suggested that human locomotion may be modeled as an inverse optimal control problem. In this paradigm, the trajectories are assumed to be solutions of an optimal control problem that has to be determined. We discuss the modeling of both the dynamical system and the cost to be minimized, and we analyze the corresponding optimal synthesis. The main results describe the asymptotic behavior of the optimal trajectories as the target point goes to infinity.