Real-time robot learning with locally weighted statistical learning

Proceedings - IEEE International Conference on Robotics and Automation

Volumn 1, Issue , 2000, Pages 288-293

  Schaal, Stefan ^a   Atkeson, Christopher G ^a   Vijayakumar, Sethu ^a  

^a UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HUMANOID ROBOT ARM; LOCALLY WEIGHTED LEARNING; STATISTICAL LEARNING TECHNIQUES;

ADAPTIVE CONTROL SYSTEMS; DEGREES OF FREEDOM (MECHANICS); LARGE SCALE SYSTEMS; LEARNING ALGORITHMS; ROBOTIC ARMS; STATISTICAL METHODS;

ROBOT LEARNING;

EID: 0033691378     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (23)

1. S. Schaal, "Is imitation learning the route to humanoid robots?," Trends in Cognitive Sciences, vol. 3, pp. 233-242, 1999.
2. C. M. Bishop, Neural networks for pattern recognition. New York: Oxford University Press, 1995.
3. C. K. I. Williams and C. E. Rasmussen, "Gaussian processes for regression," in Advances in Neural Information Processing Systems 8, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, Eds. Cambridge, MA: MIT Press, 1996, pp. 514-520.
4. V. N. Vapnik, Estimation of dependences based on empirical data. Berlin: Springer, 1982.
5. C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
6. V. Vapnik, S. Golowich, and A. Smola, "Support vector method for function approximation, regression estimation, and signal processing," in Advances in Neural Information Processing Systems 9, M. Mozer, M. I. Jordan, and T. Petsche, Eds. Cambridge, MA: MIT Press, 1996, pp. 281-287.
7. S. Schaal and C. G. Atkeson Constructive incremental learning from only local information, Neural Computation,vol 10, pp. 2047-2084, 1998.
8. C. G. Atkeson and S. Schaal, "Memory-based neural networks for robot learning," Neurocomputing, vol. 9, pp. 1-27, 1995.
9. W. S. Cleveland and C. Loader, "Smoothing by local regression: Principles and methods," : AT&T Bell Laboratories Murray Hill NY, 1995.
10. T. J. Hastie and R. J. Tibshirani, "Nonparametric regression and classification: Part I: Nonparametric regression," in From Statistics to Neural Networks: Theory and Pattern Recognition Applications. ASI Proceedings, subseries F, Computer and Systems Sciences, V. Cherkassky, J. H. Friedman, and H. Wechsler, Eds.: Springer, 1994, pp. 120-143.
11. C. G. Atkeson, A. W. Moore, and S. Schaal, "Locally weighted learning," Artijicial Intelligence Review, vol. 11, pp. 11-73, 1997.
12. C. G. Atkeson, A. W. Moore, and S. Schaal, "Locally weighted learning for control," Artificial Intelligence Review, vol. 11, pp. 75-113, 1997.
13. D. Aha, "Lazy Learning," Artificial Intelligence Review, pp. 325-337, 1997.
14. L. Ljung and T. Soderstrom, Theory and practice of recursive identification: Cambridge MIT Press, 1986.
15. W. S. Cleveland, "Robust locally weighted regression and smoothing scatterplots," Journal of the American Statistical Association, vol. 74, pp. 829-836, 1979.
16. T. Hastie and C. Loader, "Local regression: Automatic kernel carpentry, " Statistical Science, vol. 8, pp. 120-143, 1993.
17. C. G. Atkeson, "Memory-based approaches to approximating continuous functions," in Nonlinear Modeling and Forecasting, M. Casdagli and S. Eubank, Eds. Redwood City, CA: Addison Wesley, 1992,~. 503-521.
18. A. W. Moore, "Efficient memory-based learning for robot control," : Computer Laboratory University of Cambridge October 1990, 1990.
19. H. Wold, "Soft modeling by latent variables: the nonlinear iterative partial least squares approach," in Perspectives in Probability and Statistics, Papers in Honour of M. S. Bartlett, J. Gani, Ed. London: Academic Press, 1975, pp. 520-540.
20. I. E. Frank and J. H. Friedman, "A statistical view of some chemometric regression tools," Technometrics, vol. 35, pp. 109-135, 1993.
21. S. Schaal and C. G. Atkeson Robot juggling: An implementation of memory-based learning, Control Systems Magazine, vol 14, pp. 57-71, 1994.
22. J. Baillieul and D. P. Martin, "Resolution of kinematic redundancy," in Proceedings of Symposia in Applied Mathematics, vol. 41: American Mathematical Society, 1990, pp. 49-89.
23. C. H. An, C. G. Atkeson, and J. M. Hollerbach, Model-based control of a robot manipulator. Cambridge, MA: MIT Press, 1988.