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Multibody System Dynamics

, Volume 34, Issue 4, pp 307–325 | Cite as

Motion optimization using Gaussian process dynamical models

  • Hyuk Kang
  • F. C. Park
Article

Abstract

We propose an efficient method for generating suboptimal motions for multibody systems using Gaussian process dynamical models. Given a dynamical model for a multibody system, and a trial motion, a lower-dimensional Gaussian process dynamical model is fitted to the trial motion. New motions are then generated by performing a dynamic optimization in the lower-dimensional space. We introduce the notion of variance tubes as an intuitive and efficient means of restricting the optimization search space. The performance of our algorithm is evaluated through detailed case studies of raising motions for an arm and jumping motions for a humanoid.

Keywords

Robot dynamics Motion optimization Machine learning Gaussian process dynamical model 

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Mechanical and Aerospace EngineeringSeoul National UniversitySeoulKorea

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