Autonomous Robots

, Volume 12, Issue 1, pp 55–69

Statistical Learning for Humanoid Robots

  • Sethu Vijayakumar
  • Aaron D'souza
  • Tomohiro Shibata
  • Jörg Conradt
  • Stefan Schaal
Article

Abstract

The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.

motor control statistical learning dimensionality reduction inverse dynamics inverse kinematics oculomotor learning nonparametric regression 

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References

  1. An, C.H., Atkeson, C., and Hollerbach, J. 1988. Model Based Control of a Robot Manipulator, MIT Press: Cambridge, MA.Google Scholar
  2. Atkeson, C., Moore, A., and Schaal, S. 1997. Locally weighted learning. Artificial Intelligence Review, 11:76–113.Google Scholar
  3. Bishop, C. 1995. Neural Networks for Pattern Recognition, Oxford University Press: London.Google Scholar
  4. Bullock, D., Grossberg, S., and Guenther, F.H. 1993. A selforganizing neural model of motor equivalent reaching and tool use by a multijoint arm. Journal of Cognitive Neuroscience, 5(4): 408–435.Google Scholar
  5. Cruse, H. and Brüwer, M. 1987. The human arm as a redundant manipulator: The control of path and joint angles. Biological Cybernetics, 57:137–144.Google Scholar
  6. Frank, I.E. and Friedman, J.H. 1993. A statistical view of some chemometric regression tools. Technometrics, 35:109–135.Google Scholar
  7. Jordan, M.I. and Rumelhart, D.E. 1992. Supervised learning with a distal teacher. Cognitive Science, 16(3):307–354.Google Scholar
  8. Kawato, M. 1990. Feedback-error-learning neural network for supervised motor learning. In Advanced Neural Computers, R. Eckmiller (Ed.), North-Holland/Elsevier: Amsterdam, pp. 365–372.Google Scholar
  9. Liegeois, A. 1977. Automatic supervisory control of the configuration and behavior of multibody mechnisms. IEEE Transactions on Systems, Man, and Cybernetics, 7(12):868–871.Google Scholar
  10. Ljung, L. and Soderstrom, T. 1986. Theory and Practice of Recursive Identification, MIT Press: Cambridge, MA.Google Scholar
  11. Sanger, T.D. 1989. Optimal unsupervised learning in a single layer liner feedforward neural network. Neural Networks, 2:459–473.Google Scholar
  12. Saunders, C., Stitson, M.O., Weston, J., Bottou, L., Schoelkopf, B., and Smola, A. 1998. Support vector machine—Reference manual. TR CSD-TR–98–03. Department of Computer Science, Royal Holloway, University of London.Google Scholar
  13. Schaal, S. 1999. Is imitation learning the route to humanoid robots? Trends in Cognitive Sciences, 3:233–242.Google Scholar
  14. Schaal, S. and Atkeson, C.G. 1998. Constructive incremental learning from only local information. Neural Comp. 10:2047–2084.Google Scholar
  15. Schaal, S., Atkeson, C.G., and Vijayakumar, S. 2000. Real-time robot learning with locally weighted statistical learning. In Proc. International Conference on Robotics and Automation ICRA2000, pp. 288–293.Google Scholar
  16. Schaal, S., Vijayakumar, S., and Atkeson, C.G. 1998. Local dimensionality reduction. Proc. Neural Information Processing Systems, 10:633–639.Google Scholar
  17. Shibata, T. and Schaal, S. 2001. Biomimetic gaze stabilization based on feedback-error-learning with nonparametric regression networks. Neural Networks, 14(2):201–216.Google Scholar
  18. Slotine, J.E. and Li, W. 1991. Applied Nonlinear Control, Prentice Hall: Englewood cliffs, NJ.Google Scholar
  19. Tevatia, G. and Schaal, S. 2000. Inverse kinematics for humanoid robots. In Proceedings of the International Conference on Robotics and Automation (ICRA2000), San Francisco, CA.Google Scholar
  20. Vapnik, V. 1995. The Nature of Statistical Learning Theory, Springer: New York.Google Scholar
  21. Vijayakumar, S. and Schaal, S. 2000. Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional space. In Proc. International Conference on Machine Learning ICML2000, pp. 1079–1086.Google Scholar
  22. Wold, H. 1975. Soft modeling with latent variables: The nonlinear iterative partial least squares approach. Perspectives in Probability and Statistics: Papers in Honor of M.S. Bartlett, pp. 114–142. Academic Press: London.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Sethu Vijayakumar
    • 1
  • Aaron D'souza
    • 1
  • Tomohiro Shibata
    • 2
  • Jörg Conradt
    • 3
  • Stefan Schaal
    • 1
  1. 1.Computer Science & Neuroscience and Kawato Dynamic Brain ProjectUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Kawato Dynamic Brain ProjectERATO, Japan Science & Technology Corp.KyotoJapan
  3. 3.University/ETH ZurichZurichSwitzerland

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