Automated Optimization of Walking Parameters for the Nao Humanoid Robot

Open Access
Authors
  • N. Girardi
  • C. Kooijman
  • A.J. Wiggers
  • A. Visser ORCID logo
Publication date 2013
Journal BNAIC
Event 25th Belgium-Netherlands Artificial Intelligence Conference (BNAIC 2013)
Volume | Issue number 25
Pages (from-to) 72-79
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract This paper describes a framework for optimizing walking parameters for a Nao humanoid robot. In this case an omnidirectional walk is learned. The parameters are learned in simulation with an evolutionary approach. The best performance was obtained for a combination of a low mutation rate and a high crossover rate.
Document type Article
Note Proceedings title: Proceedings of the 25th Benelux conference on Artificial Intelligence Publisher: Delft University of Technology Place of publication: Delft Editors: K. Hindriks, M. de Weerdt, B. van Riemsdijk, M. Warnier
Language English
Published at http://bnaic2013.tudelft.nl/proceedings/papers/paper_69.pdf
Downloads
LearningWalkingBehaviors.pdf (Accepted author manuscript)
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