Expensive Multiobjective Optimization for Robotics - Robotics Institute Carnegie Mellon University

Expensive Multiobjective Optimization for Robotics

Matthew Tesch, Jeff Schneider, and Howie Choset
Conference Paper, Proceedings of (ICRA) International Conference on Robotics and Automation, pp. 973 - 980, May, 2013

Abstract

Many practical optimization problems in robotics involve multiple competing objectives - from design trade-offs to performance metrics of the physical system such as speed and energy efficiency. Proper treatment of these objective functions, while commonplace in fields such as economics, is often overlooked in robotics. Additionally, optimization of the performance of robotic systems can be restricted due to the expensive nature of testing control parameters on a physical system. This paper presents a multi-objective optimization (MOO) algorithm for expensive-to-evaluate functions that generates a Pareto set of solutions. This algorithm is compared against another leading MOO algorithm, and then used to optimize the speed and head stability of the sidewinding gait for a snake robot.

BibTeX

@conference{Tesch-2013-7715,
author = {Matthew Tesch and Jeff Schneider and Howie Choset},
title = {Expensive Multiobjective Optimization for Robotics},
booktitle = {Proceedings of (ICRA) International Conference on Robotics and Automation},
year = {2013},
month = {May},
pages = {973 - 980},
}
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