Date of Original Version

6-2013

Type

Conference Proceeding

Rights Management

Copyright 2013 by the author(s)

Abstract or Description

Robotic systems often have tunable parameters which can affect performance; Bayesian optimization methods provide for efficient 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 de- fine the stochastic binary optimization problem, present a Bayesian framework using Gaussian processes for classification, 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 efficient search for diffi- cult tasks. This method is used to create an adaptive policy for climbing over obstacles of varying heights.

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Robotics Commons

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Published In

Journal of Machine Learning Research : Workshop and Conference Proceedings, 28.