Learning Stochastic Binary Tasks using Bayesian Optimization with Shared Task Knowledge - Robotics Institute Carnegie Mellon University

Learning Stochastic Binary Tasks using Bayesian Optimization with Shared Task Knowledge

Workshop Paper, ICML '13 Workshop on Robot Learning, June, 2013

Abstract

Robotic systems often have tunable parameters which can aect performance; Bayesian optimization methods provide for ecient parameter optimization, reducing required tests on the robot. This paper addresses Bayesian optimization in the setting where performance is only observed through a stochastic binary outcome { success or failure. We dene the stochastic binary optimization problem, present a Bayesian framework using Gaussian processes for classication, adapt the existing expected improvement metric for the binary case, and benchmark its performance. We also exploit problem structure and task similarity to generate principled task priors allowing ecient search for dicult tasks. This method is used to create an adaptive policy for climbing over obstacles of varying heights.

BibTeX

@workshop{Tesch-2013-7744,
author = {Matthew Tesch and Jeff Schneider and Howie Choset},
title = {Learning Stochastic Binary Tasks using Bayesian Optimization with Shared Task Knowledge},
booktitle = {Proceedings of ICML '13 Workshop on Robot Learning},
year = {2013},
month = {June},
}