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research
An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion
Authors
R Calandra
MP Deisenroth
J Peters
A Seyfarth
Publication date
10 December 2013
Publisher
Doi
Cite
Abstract
© 2014 IEEE.The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting. The use of automatic gait optimization methods greatly reduces the need for human expertise and time-consuming design processes. Many different approaches to automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this paper, we present some common methods for automatic gait optimization in bipedal locomotion, and analyze their strengths and weaknesses. We experimentally evaluated these gait optimization methods on a bipedal robot, in more than 1800 experimental evaluations. In particular, we analyzed Bayesian optimization in different configurations, including various acquisition functions
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Spiral - Imperial College Digital Repository
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Last time updated on 15/03/2014
Supporting member
Spiral - Imperial College Digital Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:spiral.imperial.ac.uk:1004...
Last time updated on 22/12/2013