Learning nonparametric models for probabilistic imitation

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 521-528

  Grimes, David B ^a   Rashid, Daniel R ^a   Rao, Rajesh P N ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; BIOMECHANICAL MODEL; FORWARD MODELS; FULL BODY; GRAPHICAL MODEL; HUMAN ARM; HUMANOID ROBOT; LEARNING BY IMITATION; LEARNING METHODS; NON-PARAMETRIC; NON-PARAMETRIC MODEL; PROBABILISTIC METHODS; RAPID ACQUISITION;

ANTHROPOMORPHIC ROBOTS; BIOMECHANICS;

DYNAMICS;

EID: 63549108724     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. P. Abbeel and A. Y. Ng. Exploration and apprenticeship learning in reinforcement learning. In In Proceedings of the Twenty-first International Conference on Machine Learning, 2005.
2. C. Atkeson and S. Schaal. Robot learning from demonstration. pages 12-20, 1997.
3. A. Billard and M. Mataric. Learning human arm movements by imitation: Evaluation of a biologically-inspired connectionist architecture. Robotics and Autonomous Systems, (941), 2001.
4. M. A. Carreira-Perpinan. Mode-finding for mixtures of gaussian distributions. IEEE Trans. Pattern Anal. Mach. Intell., 22(11):1318-1323, 2000.
5. J. Demiris and G. Hayes. A robot controller using learning by imitation, 1994.
6. D. B. Grimes, R. Chalodhorn, and R. P. N. Rao. Dynamic imitation in a humanoid robot through nonparametric probabilistic inference. In Proceedings of Robotics: Science and Systems (RSS'06), Cambridge, MA, 2006. MIT Press.
7. A. T. Ihler, E. B. Sudderth, W. T. Freeman, and A. S. Willsky. Efficient multiscale sampling from products of gaussian mixtures. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural Information Processing Systems 16. MIT Press, Cambridge, MA, 2004.
8. A. J. Ijspeert, J. Nakanishi, and S. Schaal. Trajectory formation for imitation with nonlinear dynamical systems. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 752-757, 2001.
9. F. R. Kschischang, B. J. Frey, and H.-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory, 47(2):498-519, 2001.
10. W. Li and E. Todorov. Iterative linear-quadratic regulator design for nonlinear biological movement systems. In Proceedings of the 1st Int. Conf. on Informatics in Control, Automation and Robotics, volume 1, pages 222-229, 2004.
11. A. N. Meltzoff. Elements of a developmental theory of imitation. pages 19-41, 2002.
12. A. Y. Ng and S. Russell. Algorithms for inverse reinforcement learning. In Proc. 17th International Conf. on Machine Learning, pages 663-670, 2000.
13. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
14. S. Schaal, A. Ijspeert, and A. Billard. Computational approaches to motor learning by imitation. 1431:199-218, 2004.
15. D. Scott and W. Szewczyk. From kernels to mixtures. Technometrics, 43(3):323-335.
16. E. B. Sudderth, A. T. Ihler, W. T. Freeman, and A. S. Willsky. Nonparametric belief propagation. In CVPR (1), pages 605-612, 2003.
17. H.-G. Sung. Gaussian Mixture Regression and Classification. PhD thesis, Rice University, 2004.
18. Y. Weiss. Correctness of local probability propagation in graphical models with loops. Neural Computation, 12(1):1-41, 2000.
19. M. Y. Kuniyoshi and H. Inoue. "learning by watching: Extracting reusable task knowledge from visual observation of human performance" ieee transaction on robotics and automation, vol.10, no.6, pp.799-822, dec., 1994.