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

David Johan Christensen, Jørgen Christian Larsen, Kasper Stoy

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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.

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