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Learning Walking Patterns for Kinematically Complex Robots Using Evolution Strategies

  • Conference paper
Parallel Problem Solving from Nature – PPSN X (PPSN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5199))

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Abstract

Manually developing walking patterns for kinematically complex robots can be a challenging and time-consuming task. In order to automate this design process, a learning system that generates, tests, and optimizes different walking patterns is needed, as well as the ability to accurately simulate a robot and its environment. In this work, we describe a learning system that uses the CMA-ES method from evolutionary computation to learn walking patterns for a complex legged robot. The robot’s limbs are controlled using parametrized distorted sine waves, and the evolutionary algorithm optimizes the parameters of these waveforms, testing the walking patterns in a physical simulation. The best solutions evolved by this system has been transferred to and tested on a real robot, and has resulted in a gait that is superior to those previously designed by a human designer.

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© 2008 Springer-Verlag Berlin Heidelberg

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Römmermann, M., Edgington, M., Metzen, J.H., de Gea, J., Kassahun, Y., Kirchner, F. (2008). Learning Walking Patterns for Kinematically Complex Robots Using Evolution Strategies. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_108

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  • DOI: https://doi.org/10.1007/978-3-540-87700-4_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87699-1

  • Online ISBN: 978-3-540-87700-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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