Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit

David Johan Christensen, Jørgen Christian Larsen, Kasper Stoy

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters of a
central pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological and
control parameters directly on the physical robot.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems
Number of pages6
PublisherIEEE
Publication date2012
ISBN (Print)9781467317283
DOIs
Publication statusPublished - 2012
Event2012 IEEE Conference on Evolving and Adaptive Intelligent Systems - Madrid, Spain
Duration: 17 May 201218 May 2012

Conference

Conference2012 IEEE Conference on Evolving and Adaptive Intelligent Systems
CountrySpain
CityMadrid
Period17/05/201218/05/2012

Keywords

  • Actuators
  • Biology
  • Legged locomotion
  • Manuals.

Cite this

Christensen, D. J., Larsen, J. C., & Stoy, K. (2012). Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit. In Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems IEEE. https://doi.org/10.1109/EAIS.2012.6232806
Christensen, David Johan ; Larsen, Jørgen Christian ; Stoy, Kasper. / Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit. Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems. IEEE, 2012.
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title = "Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit",
abstract = "This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters of acentral pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological andcontrol parameters directly on the physical robot.",
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Christensen, DJ, Larsen, JC & Stoy, K 2012, Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit. in Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems. IEEE, 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems, Madrid, Spain, 17/05/2012. https://doi.org/10.1109/EAIS.2012.6232806

Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit. / Christensen, David Johan; Larsen, Jørgen Christian; Stoy, Kasper.

Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems. IEEE, 2012.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Christensen DJ, Larsen JC, Stoy K. Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit. In Proceedings of the IEEE Conference on Evolving and Adaptive Intelligent Systems. IEEE. 2012 https://doi.org/10.1109/EAIS.2012.6232806