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JRM Vol.19 No.4 pp. 395-401
doi: 10.20965/jrm.2007.p0395
(2007)

Paper:

An Environment Cognition and Motor Adaptation Model Eliciting Sensorimotor Constraints Based on Time-Series Observations

Toshiyuki Kondo* and Koji Ito**

*Dept. of Computer and Information Sciences, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo 184-8588, Japan

**Dept. of Computational Intelligence and Systems Science, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

Received:
January 11, 2007
Accepted:
April 24, 2007
Published:
August 20, 2007
Keywords:
environment cognition, dynamical systems, recurrent neural networks, neural oscillator model, swing of a pendulum
Abstract

Regardless of complex, diverse, dynamically changing environments, animals recognize situated worlds and respond appropriately through interaction with the environment. We investigated sentient environment cognition and motor adaptation, focusing on “automatic movement.” Thanks to periodicity, environment cognition and motor adaptation of body movement to environmental perturbations is modeled relatively easily. We propose an adaptive neural oscillator model inspired by biological brain/nervous system for periodic motion control of physical controlled objects such as pendulums. A key concept for realizing environment cognition and motor adaptation is the context-based elicitation of sensorimotor constraints canalizing suitable periodic movement. Results of control tasks demonstrated the feasibility of our proposal.

Cite this article as:
Toshiyuki Kondo and Koji Ito, “An Environment Cognition and Motor Adaptation Model Eliciting Sensorimotor Constraints Based on Time-Series Observations,” J. Robot. Mechatron., Vol.19, No.4, pp. 395-401, 2007.
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References
  1. [1] D. M. Wolpert and M. Kawato, “Multiple paired forward and inverse models for motor control,” Neural Networks, 11, pp. 1317-1329, 1998.
  2. [2] H. Haken, “Principles of Brain Functioning –A Synergetic Approach to Brain Activity, Behavior and Cognition–,” Springer-Verlag Berlin Heidelberg, 1996.
  3. [3] G. Schöner and J. A. S. Kelso, “Dynamic pattern generation in behavioral and neural systems,” Science, 239, pp. 1513-1520, 1988.
  4. [4] E. Thelen and L. B. Smith, “A Dynamic Systems Approach to the Development of Cognition and Action,” Cambridge, MA: MIT Press, 1994.
  5. [5] R. D. Beer, “Dynamical approaches to cognitive science,” Trend in Cognitive Sciences, 4-3, pp. 91-99, 2000.
  6. [6] J. Tani, “Learning to generate articulated behavior through the bottom-up and the top-down interaction processes,” Neural Networks, 16, pp. 11-23, 2003.
  7. [7] M. Okada, K. Osato, and Y. Nakamura, “Motion Emergency of Humanoid Robots by an Attractor Design of a Nonlinear Dynamics,” Proc. of the IEEE International Conference on Robotics and Automation (ICRA’05), pp. 18-23, 2005.
  8. [8] A. J. Ijspeert, J. Nakanishi, and S. Schaal, “Movement imitation with nonlinear dynamical systems in humanoid robots,” Proc. of the IEEE International Conference on Robotics and Automation (ICRA’02), pp. 1398-1403, 2002.
  9. [9] J. Tani, M. Ito, and Y. Sugita, “Self-organization of distributedly represented multiple behavior schemata in a mirror system: review of robot experiments using RNNPB,” Neural Networks, 17, pp. 1273-1289, 2004.
  10. [10] H. Asama, M. Yano, K. Tsuchiya, K. Ito, H. Yuasa, J. Ota, A. Ishiguro, and T. Kondo, “System Principle on Emergence of Mobiligence and Its Engineering Realization,” Proc. of 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’03), pp. 1715-1720, 2003.
  11. [11] S. Grillner, “The motor infrastructure: from ion channels to neuronal networks,” Nature Reviews Neuroscience, 4, pp. 573-586, 2003.
  12. [12] D. Yanagihara, M. Udo, I. Kondo, and T. Yoshida, “A new learning paradigm: adaptive changes in interlimb coordination during perturbed locomotion in decerebrate cats,” Neurosci Res., 18, pp. 241-244, 1993.
  13. [13] K. Matsuoka, “Mechanisms of frequence and pattern control in the neural rhythm generators,” Biological Cybernetics, 56, pp. 345-353, 1987.
  14. [14] G. Taga, “A model of the neuro-musculo-skeletal system for human locomotion,” Biological Cybernetics, 73-1, pp. 97-111, 1995.
  15. [15] H. Kimura and Y. Fukuoka, “Biologically Inspired Adaptive Dynamic Walking in Outdoor Environment Using a Self-contained Quadruped Robot: ‘Tekken2’,” Proc. of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’04), pp. 986-991, 2004.
  16. [16] T. Kondo, T. Somei, and K. Ito, “A predictive constraints selection model for periodic motion pattern generation,” Proc. of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’04), pp. 975-980, 2004.
  17. [17] D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Parallel Distributed Processing,” 1, The MIT Press, 1988.
  18. [18] S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by simulated annealing,” Science, 220-4598, pp. 671-680, 1983.
  19. [19] R. Smith, “Open dynamics engine v0.5 user guide,” 2004.
    http://ode.org/
  20. [20] E. Bizzi, F. A. Mussa-Ivaldi, and S. F. Giszter, “Computations underlying the execution of movement: a biological perspective,” Science, 253, pp. 287-291, 1991.
  21. [21] W. J. Freeman, “Simulation of Chaotic EEG Patterns with a Dynamic Model of the Olfactory System,” Biological Cybernetics, 56, pp. 139-150, 1987.
  22. [22] K. Kojima and K. Ito, “Autonomous Learning Algorithm and Associative Memory for Intelligent Robots,” Proc. of IEEE International Conference on Robotics and Automation (ICRA’01), pp. 505-510, 2001.
  23. [23] I. Tsuda, “A hermeneutic process of the Brain,” Progress of Theoretical Physics, Supplement, 79, pp. 241-259, 1984.
  24. [24] J. Hawkins and S. Blakeslee, “On Intelligence,” Times Books, 2004.

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