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2014 | OriginalPaper | Buchkapitel

Bayesian Gait Optimization for Bipedal Locomotion

verfasst von : Roberto Calandra, Nakul Gopalan, André Seyfarth, Jan Peters, Marc Peter Deisenroth

Erschienen in: Learning and Intelligent Optimization

Verlag: Springer International Publishing

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Abstract

One of the key challenges in robotic bipedal locomotion is finding gait parameters that optimize a desired performance criterion, such as speed, robustness or energy efficiency. Typically, gait optimization requires extensive robot experiments and specific expert knowledge. We propose to apply data-driven machine learning to automate and speed up the process of gait optimization. In particular, we use Bayesian optimization to efficiently find gait parameters that optimize the desired performance metric. As a proof of concept we demonstrate that Bayesian optimization is near-optimal in a classical stochastic optimal control framework. Moreover, we validate our approach to Bayesian gait optimization on a low-cost and fragile real bipedal walker and show that good walking gaits can be efficiently found by Bayesian optimization.

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Vorheriges Kapitel A Hybrid Clonal Selection Algorithm for the Vehicle Routing Problem with Stochastic Demands

Nächstes Kapitel Robust Support Vector Machines with Polyhedral Uncertainty of the Input Data

The correct notation would be \(\alpha (\hat{f}(\varvec{\theta }))\), but we use \(\alpha (\varvec{\theta })\) for notational convenience.

Videos are available at http://www.ias.tu-darmstadt.de/Research/Fox.

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Titel: Bayesian Gait Optimization for Bipedal Locomotion
verfasst von: Roberto Calandra
Nakul Gopalan
André Seyfarth
Jan Peters
Marc Peter Deisenroth
Verlag: Springer International Publishing
Buch: Learning and Intelligent Optimization
Print ISBN: 978-3-319-09583-7

Electronic ISBN: 978-3-319-09584-4

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-319-09584-4_25

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