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An Algorithm That Recognizes and Reproduces Distinct Types of Humanoid Motion Based on Periodically-Constrained Nonlinear PCA

  • Rawichote Chalodhorn
  • Karl MacDorman
  • Minoru Asada
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3276)

Abstract

This paper proposes a new algorithm for the automatic segmentation of motion data from a humanoid soccer playing robot that allows feed-forward neural networks to generalize and reproduce various kinematic patterns, including walking, turning, and sidestepping. Data from a 20 degree-of-freedom Fujitsu hoap-1 robot is reduced to its intrinsic dimensionality, as determined by the isomap procedure, by means of nonlinear principal component analysis (nlpca). The proposed algorithm then automatically segments motion patterns by incrementally generating periodic temporally-constrained nonlinear pca neural networks and assigning data points to these networks in a conquer-and-divide fashion, that is, each network’s ability to learn the data influences the data’s division among the networks. The learned networks abstract five out of six types of motion without any prior information about the number or type of motion patterns. The multiple decoding subnetworks that result can serve to generate abstract actions for playing soccer and other complex tasks.

Keywords

Periodic Motion Humanoid Robot Automatic Segmentation Intrinsic Dimensionality Nonlinear Principal Component Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rawichote Chalodhorn
    • 1
  • Karl MacDorman
    • 1
  • Minoru Asada
    • 1
  1. 1.Department of Adaptive Machine Systems and Frontier Research Center, Graduate School of EngineeringOsaka UniversityOsakaJapan

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