Echo state networks for mobile robot modeling and control

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3020, Issue , 2004, Pages 157-168

  Plöger, Paul G ^a   Arghir, Adriana ^a   Günther, Tobias ^a   Hosseiny, Ramin ^a  

^a FHG Institute of Autonomous Intelligent Systems Schloss Birlinghoven   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ROBOTS; SPORTS;

DIFFERENTIAL DRIVE ROBOTS; DYNAMICAL MODEL; ECHO STATE NETWORKS; MOTOR CONTROLLERS; RECURRENT NEURAL NETWORK (RNNS); ROBOT MODEL; VERSATILE TOOLS;

RECURRENT NEURAL NETWORKS;

EID: 84943251123     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-25940-4_14     Document Type: Conference Paper

References (22)

1. Christaller Th. Jaeger H Kobialka H.-U Schoell P Bredenfeld A. Robot behavior design using dual dynamics. Tech. GMD Report, 2000.
2. R. C. Arkin. Behavior-based robotics. The MIT Press, 1998.
3. Ansgar Bredenfeld and Hans-Ulrich Kobialka. Team cooperation using dual dynamics. In Markus Hannebauer, editor, Balancing reactivity and social deliberation in multi-agent systems, Lecture notes in computer science, pages 111-124, 2001.
4. Yoshua Bengio, P. Simard, and P. Frasconi. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2):157-166, 1994. (Special Issue Dynamic and Recurrent Neural Networks.).
5. C. R. Gallistel. The Organization of Action: a New Synthesis. Lawrence Erlbaum Associates, Inc., Hilldale, NJ., 1980.
6. Ramin Housseiny. Echo state networks used for the classification and filtering of silicon retina signals. Master's thesis, RWTH AAchen, 2003.
7. H. Jaeger. The "echo state" approach to analysing and training recurrent neural networks. GMD Report 148, pages 1-43, 2001.
8. Herbert Jaeger. The echo state approach to analysing and training recurrent neural networks. Technical report, GMD-Forschungszentrum Informationstechnik GmbH, 2001.
9. Herbert Jaeger. Short term memory in echo state networks. Technical report, GMD Forschungszentrum Informationstechnik GmbH, 2001.
10. Herbert Jaeger. Tutorial on trainig recurrent neural networks covering bppt, rtrl, ekf and the "echo state network" approach. Technical report, GMD Forschungszentrum Informationstechnik GmbH, 2002.
11. M.I. Jordan and Rumelhart D.E. Forward models: Supervised learning with a distal teacher, 1992.
12. M.I. Jordan. Computational aspects of motor control and motor learning. In H. Heuer and S. Keele, editors, Handbook of Perception and Action: Motor Skills. Academic Press, New York, 1995.
13. D. Kortenkamp, R. P. Bonasso, and R. Murphy. Artificial Intelligence and Mobile Robots. AAAI Press/The MIT Press, 1998.
14. Furawaka K. Kawato, M. and R. Suzuki. A hierarchical neural network model for the control learning of voluntary movements, 1987.
15. Carver Mead. Analog VLSI and Neural Systems. Addison-Wesley, Reading, MA, USA, 1989.
16. W. Maass, T. Natschläger, and H. Markram. Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation, 2002.
17. R.C. Miall and D.M. Wolpert. Forward models for phisiological motor control, 1996.
18. Dean A. Pomerleau. Neural Network Perception for Mobile Robot Guidance. Kluwer, Dordrecht, The Netherlands, 1993.
19. William H. Press, Saul A. Teukolsky, William T. Vetterling, and Brian P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, Cambridge, 2 edition, 1992.
20. R.S. Sutton and A.G. Barto. Toward a modern theory of adaptive networks: expectation and prediction., 1981.
21. Frank Schoenherr. Learning to ground fact symbols in behavior-based robots. In F. van Harmelen, editor, Proceedings of the 15th European Conference on Artificial Intelligence, pages 708-712. ECAI, IOS Press, 2002.
22. Johan A.K. Suykens and Joos Vandewalle, editors. Nonlinear modeling, chapter Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks. Kluwer, 1998.