Distributed online learning of central pattern generators in modular robots

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

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Distributed online learning of central pattern generators in modular robots. / Christensen, David Johan; Spröwitz, Alexander; Ijspeert, Auke Jan.

From Animals to Animats 11. ed. / R. Goebel; J. Siekmann; W. Wahlster. Springer, 2010. p. 402-412 (Lecture Notes in Computer Science; No. 6226).

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

Harvard

Christensen, DJ, Spröwitz, A & Ijspeert, AJ 2010, 'Distributed online learning of central pattern generators in modular robots'. in R Goebel, J Siekmann & W Wahlster (eds), From Animals to Animats 11. Springer, pp. 402-412. Lecture Notes in Computer Science, no. 6226, , 10.1007/978-3-642-15193-4_38

APA

Christensen, D. J., Spröwitz, A., & Ijspeert, A. J. (2010). Distributed online learning of central pattern generators in modular robots. In R. Goebel, J. Siekmann, & W. Wahlster (Eds.), From Animals to Animats 11. (pp. 402-412). Springer. (Lecture Notes in Computer Science; No. 6226). 10.1007/978-3-642-15193-4_38

CBE

Christensen DJ, Spröwitz A, Ijspeert AJ. 2010. Distributed online learning of central pattern generators in modular robots. Goebel R, Siekmann J, Wahlster W, editors. In From Animals to Animats 11. Springer. pp. 402-412. (Lecture Notes in Computer Science; No. 6226). Available from: 10.1007/978-3-642-15193-4_38

MLA

Christensen, David Johan, Alexander Spröwitz and Auke Jan Ijspeert "Distributed online learning of central pattern generators in modular robots"., Goebel, R. Siekmann, J. Wahlster, W. (ed.). From Animals to Animats 11. Springer. 2010. 402-412. (Lecture Notes in Computer Science; Journal number 6226). Available: 10.1007/978-3-642-15193-4_38

Vancouver

Christensen DJ, Spröwitz A, Ijspeert AJ. Distributed online learning of central pattern generators in modular robots. In Goebel R, Siekmann J, Wahlster W, editors, From Animals to Animats 11. Springer. 2010. p. 402-412. (Lecture Notes in Computer Science; No. 6226). Available from: 10.1007/978-3-642-15193-4_38

Author

Christensen, David Johan; Spröwitz, Alexander; Ijspeert, Auke Jan / Distributed online learning of central pattern generators in modular robots.

From Animals to Animats 11. ed. / R. Goebel; J. Siekmann; W. Wahlster. Springer, 2010. p. 402-412 (Lecture Notes in Computer Science; No. 6226).

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

Bibtex

@inbook{a7fea600d4cc4ad4a4e4eab3f0770745,
title = "Distributed online learning of central pattern generators in modular robots",
publisher = "Springer",
author = "Christensen, {David Johan} and Alexander Spröwitz and Ijspeert, {Auke Jan}",
year = "2010",
doi = "10.1007/978-3-642-15193-4_38",
editor = "R. Goebel and J. Siekmann and W. Wahlster",
series = "Lecture Notes in Computer Science",
pages = "402-412",
booktitle = "From Animals to Animats 11",

}

RIS

TY - GEN

T1 - Distributed online learning of central pattern generators in modular robots

A1 - Christensen,David Johan

A1 - Spröwitz,Alexander

A1 - Ijspeert,Auke Jan

AU - Christensen,David Johan

AU - Spröwitz,Alexander

AU - Ijspeert,Auke Jan

PB - Springer

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-15193-4_38

DO - 10.1007/978-3-642-15193-4_38

BT - From Animals to Animats 11

T2 - From Animals to Animats 11

A2 - Wahlster,W.

ED - Wahlster,W.

T3 - Lecture Notes in Computer Science

T3 - en_GB

SP - 402

EP - 412

ER -