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)


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.


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.


  1. 1.
    Inamura, T., Toshima, I., Nakamura, Y.: Acquiring motion elements for bidirectional computation of motion recognition and generation. In: Siciliano, B., Dario, P. (eds.) Experimental Robotics VIII, pp. 372–381. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Fujii, T.: A new approach to the LQ design from the viewpoint of the inverse regulator problem. IEEE Transactions on Automatic Control 32, 995–1004 (1987)zbMATHCrossRefGoogle Scholar
  3. 3.
    Zatsiorsky, V.M.: Kinematics of Human Motion. Human Kinetics, Urbana Champaign (2002)Google Scholar
  4. 4.
    Okada, M., Tatani, K., Nakamura, Y.: Polynomial design of the nonlinear dynamics for the brain-like information processing of whole body motion. In: IEEE International Conference on Robotics and Automation, pp. 1410–1415 (2002)Google Scholar
  5. 5.
    Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. Journal of the American Institute of Chemical Engineers 37, 233–243 (1991)Google Scholar
  6. 6.
    Tatani, K., Nakamura, Y.: Dimensionality reduction and reproduction with hierarchical NLPCA neural networks extracting common space of multiple humanoid motion patterns. In: Proceedings of the IEEE International Conference on Robotics and Automation, Taipei, Taiwan, pp. 1927–1932 (2003)Google Scholar
  7. 7.
    Malthouse, E.C.: Limitations of nonlinear PCA as performed with generic neural networks. IEEE Transactions on Neural Networks 9, 165–173 (1998)CrossRefGoogle Scholar
  8. 8.
    Ridella, S., Rovetta, S., Zunino, R.: Adaptive internal representation in circular backpropagation networks. Neural Computing and Applications 3, 222–333 (1995)CrossRefGoogle Scholar
  9. 9.
    Ridella, S., Rovetta, S., Zunino, R.: Circular backpropagation networks for classification. IEEE Transaction on Neural Networks 8, 84–97 (1997)CrossRefGoogle Scholar
  10. 10.
    Kirby, M.J., Miranda, R.: Circular nodes in neural networks. Neural Computation 8, 390–402 (1996)CrossRefGoogle Scholar
  11. 11.
    LeCun, Y., Bottou, L., Orr, G.B., Müller, K.R.: Efficient BackProp. In: Orr, G.B., Müller, K.R. (eds.) Neural Networks: Tricks of the Trade, pp. 1–44. Springer, Heidelberg (1998)Google Scholar
  12. 12.
    MacKay, D.J.: Probable networks and plausible predictions: A review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems 6, 469–505 (1995)zbMATHCrossRefGoogle Scholar
  13. 13.
    Chalodhorn, R., Aono, M., Ooga, J., Ogino, M., Asada, M.: Osaka University “Senchans 2003”. In Browning, B., Polani, D., Bonarini, A., Yoshida, K., eds.: RoboCup-2003: Robot Soccer World Cup VII, Springer Verlag (2003) Google Scholar
  14. 14.
    Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000)CrossRefGoogle Scholar
  15. 15.
    Chalodhorn, R., MacDorman, K., Asada, M.: Automatic extraction of abstract actions from humanoid motion data. In: IROS 2004: IEEE/RSJ International Conference on Intelligent Robots and Systems, Sendai, Japan (Submitted)Google Scholar
  16. 16.
    MacDorman, K., Chalodhorn, R., Ishiguro, H., Asada, M.: Protosymbols that integrate recognition and response. In: EpiRob 2004: Fourth International Workshop on Epigenetic Robotics, Genoa, Italy (2004)Google Scholar
  17. 17.
    MacDorman, K., Chalodhorn, R., Asada, M.: Periodic nonlinear principal component neural networks for humanoid motion segmentation, generalization, and generation. In: ICPR 2004: International Conference on Pattern Recognition, Cambridge, UK (2004)Google Scholar
  18. 18.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)Google Scholar

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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