Compressed Sparse Code Hierarchical SOM on learning and reproducing gestures in humanoid robots

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication

Volumn , Issue , 2010, Pages 330-335

  Pierris, Georgios ^a   Dahl, Torbjørn S ^a  

^a UNIVERSITY OF GLAMORGAN   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVITY LEVELS; ACTIVITY LIST; CLASSIFICATION AND RECOGNITION; COGNITIVE MODEL; COMPARATIVE ANALYSIS; GAUSSIAN MIXTURE MODEL; HUMANOID ROBOT; RESEARCH AREAS; SELF-ORGANIZING SYSTEMS; SHORT TERM MEMORY; SPARSE CODES; TRAINING DATA; TRAINING PHASE;

ANTHROPOMORPHIC ROBOTS; COGNITIVE SYSTEMS; CONFORMAL MAPPING; ENCODING (SYMBOLS); GESTURE RECOGNITION;

HIERARCHICAL SYSTEMS;

EID: 78649865928     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROMAN.2010.5598654     Document Type: Conference Paper

References (24)

1. N. E. Sharkey, "Biologically inspired robotics," Handbook of Brain Theory and Neural Networks, 2002.
2. L. B. Cohen, H. H. Chaput, and C. H. Cashon, "A constructivist model of infant cognition," Cognitive Development, vol. 17, no. 3- 4, pp. 1323-1343, 2002.
3. S. Grossberg, Neural networks and natural intelligence. Cambridge, MA, US: The MIT Press, 1988.
4. J. Lampinen and E. Oja, "Clustering properties of hierarchical selforganizing maps," Journal of Mathematical Imaging and Vision, vol. 2, no. 2-3, pp. 261-272, 1992.
5. T. S. Dahl, "Hierarchical traces for reduced nsm memory requirements," in the Proceedings of the BCS SGAI International Conference on Artificial Intelligence, Cambridge, United Kingdom, 2010.
6. Y. Demiris and A. Billard, "Special issue on robot learning by observation, demonstration,and imitation," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 37, no. 2, pp. 254-255, 2007.
7. D. Gouaillier and P. Blazevic, "A mechatronic platform, the Aldebaran robotics humanoid robot," 32nd IEEE Annual Conference on Industrial Electronics, IECON 2006, pp. 4049-4053, November 2006.
8. G. Pierris and M. Lagoudakis, "An interactive tool for designing complex robot motion patterns," IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan, May 2009.
9. A. Billard and R. Siegwart, "Robot learning from demonstration," Robotics and Autonomous Systems, vol. 47, no. 2-3, pp. 65-67, 2004.
10. S. Calinon, Robot Programming by Demonstration: A Probabilistic Approach. EPFL/CRC Press, 2009.
11. N. Otero, A. Alissandrakis, K. Dautenhahn, C. L. Nehaniv, D. S. Syrdal, and K. L. Koay, "Human to robot demonstrations of routine home tasks: exploring the role of the robot's feedback," in HRI, 2008.
12. T. Kohonen, M. R. Schroeder, and T. S. Huang, Eds., Self-Organizing Maps. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2001.
13. A. Shimada and R. ichiro Taniguchi, "Gesture recognition using sparse code of hierarchical som," in ICPR, 2008, pp. 1-4.
14. S. Okada and O. Hasegawa, "Classification of temporal data based on self-organizing incremental neural network," in ICANN (2), 2007, pp. 465-475.
15. T. Ogata, K. Hayashi, I. Kitagishi, and S. Sugano, "Generation of behavior automaton on neural network," in Intelligent Robots and Systems, 1997. IROS '97., Proceedings of the 1997 IEEE/RSJ International Conference on, vol. 2, 7-11 1997, pp. 608-613 vol.2.
16. M. Ito and J. Tani, "Generalization in learning multiple temporal patterns using rnnpb," in Neural Information Processing. Springer, 2004, pp. 592-598.
17. M. Ito, K. Noda, Y. Hoshino, and J. Tani, "Dynamic and interactive generation of object handling behaviors by a small humanoid robot using a dynamic neural network model," Neural Networks, vol. 19, no. 3, pp. 323-337, 2006.
18. C. Gallicchio and A. Micheli, "On the Predictive Effects of Markovian and Architectural Factors of Echo State Networks," 2009.
19. Y. Bengio, P. Frasconi, and P. Simard, "The problem of learning longterm dependencies in recurrent networks," in Proceedings of the IEEE International Conference on Neural Networks, 1993.
20. H. Jaeger, "The echo state approach to analysing and training recurrent neural networks," 2001.
21. C. Hartland and N. Bredeche, "Using echo state networks for robot navigation behavior acquisition," 2007.
22. H. Jaeger, "Tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the echo state network approach," Fraunhofer Institute for Autonomous Intelligent Systems (AIS), International University Bremen, 2002.
23. Q. Song and Z. Feng, "Stable trajectory generator-echo state network trained by particle swarm optimization," in 2009 IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), 2009, pp. 21-26.
24. D. J. Berndt and J. Clifford, "Using dynamic time warping to find patterns in time series," in KDD Workshop, 1994, pp. 359-370.