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

Authors
Publisher
IEEE
Publication Date
Keywords
  • 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
  • We Observe That The Strategy Converges In Roughly Ten Minutes To Gaits Of Similar Or Higher Velocity
  • In Future Work We Plan To Study Co-Learning Of Morphological And<Br/>Control Parameters Directly On

Abstract

Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit - DTU Orbit (27/05/14) Adaptive Strategy for Online Gait Learning Evaluated on the Polymorphic Robotic LocoKit - DTU Orbit (27/05/14) 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. Publication: Research - peer-review › Article in proceedings – Annual report year: 2012

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