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An experimental comparison of Bayesian optimization for bipedal locomotion

Roberto Calandra; André Seyfarth; Jan Peters; Marc Peter Deisenroth
In: 2014 IEEE International Conference on Robotics and Automation. IEEE International Conference on Robotics and Automation (ICRA-2014), May 31 - June 7, Hong Kong, China, Pages 1951-1958, IEEE, 2014.


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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