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Paper TuB02.4

Puydupin-Jamin, Anne-Sophie (University of Illinois at Urbana Champaign), Johnson, Miles (University oif Illinois at Urbana Champaign), Bretl, Timothy (University of Illinois at Urbana-Champaign)

A Convex Approach to Inverse Optimal Control and Its Application to Modeling Human Locomotion

Scheduled for presentation during the Regular Session "Human Like Biped Locamotion" (TuB02), Tuesday, May 15, 2012, 11:15−11:30, Meeting Room 2 (Chief Red Wing)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Motion and Path Planning, Humanoid and Bipedal Locomotion, Learning and Adaptive Systems

Abstract

Inverse optimal control is the problem of computing a cost function that would have resulted in an observed sequence of decisions. The standard formulation of this problem assumes that decisions are optimal and tries to minimize the difference between what was observed and what would have been observed given a candidate cost function. We assume instead that decisions are only approximately optimal and try to minimize the extent to which observed decisions violate first-order necessary conditions for optimality. For a discrete-time optimal control system with a cost function that is a linear combination of known basis functions, this formulation leads to an efficient method of solution as a single quadratic program. We apply this approach to both simulated and experimental data to obtain a simple model of human walking paths. This model might subsequently be used either for control of a humanoid robot or for predicting human motion when moving a robot through crowded areas.

 

 

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