Supervised learning of internal models for autonomous goal-oriented robot navigation using reservoir computing

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2010, Pages 2959-2964

  Antonelo, Eric A ^a   Schrauwen, Benjamin ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

DISTANCE SENSORS; FEATURE SPACE; GOAL-ORIENTED; HIERARCHICAL ARCHITECTURES; IMITATION LEARNING; INFRA RED; INTERNAL MODELS; PROPOSED ARCHITECTURES; RESERVOIR COMPUTING; ROBOT ENVIRONMENT; ROBOT NAVIGATION; SIMULATED ROBOT; TEMPORAL KERNEL; UNKNOWN ENVIRONMENTS;

EVOLUTIONARY ALGORITHMS; NAVIGATION; NETWORK ARCHITECTURE; RECURRENT NEURAL NETWORKS; ROBOTICS; SENSORS;

ROBOTS;

EID: 77955794361     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2010.5509212     Document Type: Conference Paper

References (26)

1. E. A. Antonelo and B. Schrauwen. Towards autonomous self-localization of small mobile robots using reservoir computing and slow feature analysis. In IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2009.
2. E. A. Antonelo, B. Schrauwen, and D. Stroobandt. Event detection and localization for small mobile robots using reservoir computing. Neural Networks, 21:862-871, 2008.
3. E. A. Antonelo, B. Schrauwen, and D. Stroobandt. Mobile robot control in the road sign problem using reservoir computing networks. In IEEE Int. Conf. on Robotics and Automation (ICRA), 2008.
4. A. Arleo, F. Smeraldi, and W. Gerstner. Cognitive navigation based on nonuniform gabor space sampling, unsupervised growing networks, and reinforcement learning. IEEE Transactions on Neural Networks, 15(3):639-652, May 2004.
5. T. Bailey and H. Durrant-Whyte. Simultaneous localisation and mapping (SLAM): Part ii state of the art. Robotics and Automation Magazine, pages 108-117, September 2006.
6. V. Braitenberg. Vehicles: Experiments in synthetic psychology. MIT Press, 1984.
7. R. Chavarriaga, T. Strsslin, D. Sheynikhovich, and W. Gerstner. A computational model of parallel navigation systems in rodents. Neuroinformatics, 3:223-241, 2005. (Pubitemid 41114886)
8. H. Jaeger. The "echo state" approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Center for Information Technology, 2001.
9. H. Jaeger and H. Haas. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless telecommunication. Science, 308:78-80, April 2 2004.
10. P. Joshi and W. Maass. Movement generation with circuits of spiking neurons. Neural Computation, 17(8):1715-1738, 2005.
11. W. Maass, T. Natschläger, and H. Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 14(11):2531-2560, 2002.
12. O. Michel. Webots: Professional mobile robot simulation. Journal of Advanced Robotics Systems, 1(1):39-42, 2004.
13. M. Milford, R. Schulz, D. Prasser, G. Wyeth, and J. Wiles. Learning spatial concepts from RatSLAM representations. Robot. Auton. Syst., 55(5):403-410, 2007.
14. F. Mondada. E-puck education robot, September 2007. http://www.e-puck. org/.
15. J. O'Keefe and J. Dostrovsky. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34:171-175, 1971.
16. P. G. Plöger, A. Arghir, T. Günther, and R. Hosseiny. Echo state networks for mobile robot modeling and control. In RoboCup 2003: Robot Soccer World Cup VII, pages 157-168, 2004.
17. M. Salmen and P. G. Plöger. Echo state networks used for motor control. In Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pages 1953-1958, 2005.
18. B. Schrauwen, D. Verstraeten, and J. Van Campenhout. An overview of reservoir computing: theory, applications and implementations. In Proceedings of the European Symposium on Artifical Neural Networks (ESANN), 2007.
19. D. Silver, J. A. D. Bagnell, and A. T. Stentz. Perceptual interpretation for autonomous navigation through dynamic imitation learning. In International Symposium of Robotics Research, August 2009.
20. T. Stroesslin, D. Sheynikhovich, R. Chavarriaga, and W. Gerstner. Robust self-localisation and navigation based on hippocampal place cells. Neural Networks, 18(9):1125-1140, 2005.
21. J. Tani. On the interactions between top-down anticipation and bottom-up regression. Frontiers in Neurorobotics, 1, 2007.
22. T. van der Zant, V. Becanovic, K. Ishii, H. Kobialka, and P. Plöger. Finding good echo state networks to control an underwater robot using evolutionary computations. In Proceedings of the 5th IFAC symposium on Intelligent Autonomous Vehicles (IAV04), 2004.
23. D. Verstraeten, B. Schrauwen, M. D'Haene, and D. Stroobandt. A unifying comparison of reservoir computing methods. Neural Networks, 20:391-403, 2007.
24. T. Waegeman, E. Antonelo, F. wyffels, and B. Schrauwen. Modular reservoir computing networks for imitation learning of multiple robot behaviors. In Proc. of the IEEE Int. Symp. on Computational Intelligence in Robotics and Automation (CIRA), 2009.
25. Y. Yamashita and J. Tani. Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Comput Biol, 4(11):e1000220, 11 2008.
26. T. Yamazaki and S. Tanaka. The cerebellum as a liquid state machine. Neural Networks, 20:290-297, 2007.