Distributed online learning of central pattern generators in modular robots

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2010

Without internal affiliation

  • Author: Christensen, David Johan

    Unknown

  • Author: Spröwitz, Alexander

    École Polytechnique Fédérale de Lausanne

  • Author: Ijspeert, Auke Jan

    École Polytechnique Fédérale de Lausanne

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In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic approximation method, SPSA, which optimizes the parameters of coupled oscillators used to generate periodic actuation patterns. The strategy is implemented in a distributed fashion, based on a globally shared reward signal, but otherwise utilizing local communication only. In a physics-based simulation of modular Roombots robots we experiment with online learning of gaits and study the effects of: module failures, different robot morphologies, and rough terrains. The experiments demonstrate fast online learning, typically 5-30 min. for convergence to high performing gaits (≈ 30 cm/sec), despite high numbers of open parameters (45-54). We conclude that the proposed approach is efficient, effective and a promising candidate for online learning on many other robotic platforms.
Original languageEnglish
Title of host publicationFrom Animals to Animats 11
EditorsR. Goebel, J. Siekmann, W. Wahlster
PublisherSpringer
Publication date2010
Pages402-412
DOIs
StatePublished

Conference

Conference11th International Conference on Simulation of Adaptive Behavior
Number11
CountryFrance
CityParis
Period25/08/1028/08/10
NameLecture Notes in Computer Science
Number6226
ISSN (Print)0302-9743
CitationsWeb of Science® Times Cited: No match on DOI
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