JRM Vol.19 No.4 pp. 474-481
doi: 10.20965/jrm.2007.p0474


Feed-Forward Adaptation to a Varying Dynamic Environment During Reaching Movements

Koji Ito*, Makoto Doi**, and Toshiyuki Kondo***

*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, 4259-G3-50 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**DENSO Corporation, 1-1 Showa-cho, Kariya, Aichi 448-8661, Japan

***Department of Computer, Information and Communication Sciences, Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, Japan

January 12, 2007
April 17, 2007
August 20, 2007
reaching movement, sensory-motor function, dynamical environment, feed-forward adaptation

Humans must compensate for the reaction forces arising from interaction with the physical environment. Recent studies have shown that humans can acquire a neural representation of the relationship between motor commands and movement, i.e. learn an internal model of environmental dynamics. We discuss feed-forward adaptation in a varying dynamic environment during reaching movements. Subjects first learned to move in a position-dependent divergent force field (DF) and velocity-dependent force field (VF), then move in a switched force field SF1 (DF→VF) and SF2 (VF→DF). The experimental results show that adaptation to switched force fields is achieved by programming the internal model control and impedance control in a feed-forward manner.

Cite this article as:
Koji Ito, Makoto Doi, and Toshiyuki Kondo, “Feed-Forward Adaptation to a Varying Dynamic Environment During Reaching Movements,” J. Robot. Mechatron., Vol.19, No.4, pp. 474-481, 2007.
Data files:
  1. [1] R. Shadmehr and S. P. Wise, “The Computational Neurobiology of Reaching and Pointing,” The MIT press, 2005.
  2. [2] K. Ito, “Systems Theory of Embodied Motor Intelligence –Motor Learning and Control for Human-Robotics–,” Kyoritsu Pub., 2005 (in Japanese).
  3. [3] R. Shadmehr and F. A. Mussa-Ivaldi, “Adaptive representation of dynamics during learning of a motor task,” J. Neuroscience, 14, 5, pp. 3208-3224, 1994.
  4. [4] F. A. Mussa Ivaldi, N. Hogan, and E. Bizzi, “Neural, mechanical, and geometric factors subserving arm posture in humans,” J. of Neuroscience, 5-10, pp. 2732-2743, 1985.
  5. [5] T. Tsuji, P. G. Morasso, K. Goto, and K. Ito, “Human hand impedance characteristics during maintained posture,” Biol. Cybern., 72, pp. 475-485, 1995.
  6. [6] H. Gomi and M. Kawato, “Human arm stiffness and equilibriumpoint trajectory during multi-joint movement,” Biol. Cybern., 76, pp. 163-171, 1997.
  7. [7] J.W. Krakauer, M. F. Ghilardi, and C. Ghez, “Independent learning of internal models for kinematic and dynamic control of reaching,” Nature neuroscience, 2-11, pp. 1026-1031, 1999.
  8. [8] E. Burdet, R. Osu, D. Franklin, T. Milner, and M. Kawato, “The central nervous system stabilizes unstable dynamics by learning optimal impedance,” Nature, 414, pp. 446-449, 2001.
  9. [9] C. D. Takahashi, R. A. Scheidt, and D. J. Reinkensmeyer, “Impedance control and internal model formation when reaching in a randomly varying dynamical environment,” J. Neurophysiol., 86, pp. 1047-1051, 2001.
  10. [10] R. Osu, E. Burdet, D. W. Franklin, T. E. Milner, and M. Kawato, “Different Mechanisms Involved in Adaptation to Stable and Unstable Dynamics,” J. Neurophysiol., 90, pp. 3255-3269, 2003.
  11. [11] D. W. Franklin, R. Osu, E. Burdet, M. Kawato, and T. E. Milner, “Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamic model,” J. Neurophysiol., 90, pp. 3270-3282, 2003.
  12. [12] M. Ito, “The Cerebellum and Neural Control,” Raven Press, 1984.
  13. [13] K. Takakusaki, T. Habaguchi, J. Ohinata-Sugimoto, K. Saitoh, and T. Sakamoto, “Basal ganglia efferents to the brainstem centers controlling postural muscle tone and locomotion: A new concept for underatanding motor disorders in basal ganblia dysfunction,” Neuroscience, 119, pp. 293-308, 2003.
  14. [14] K. Takakusaki, K. Saito, H. Harada, and K. Kashiwayanagi, “Role of basal ganglia-brainstem pathways in the control of motor behaviors,” Neuroscience Research, 50, pp. 137-151, 2004.
  15. [15] S. Kitazawa, T. Kimura, and P. B. Yin, “Cerebellar Complex Spikes Encodes Both Destinations and Errors in Arm Movements,” Nature, 392, pp. 494-497, 1998.
  16. [16] H. Imamizu, S. Miyauchi, T. Tamada, Y. Sasaki, R. Takino, B. Puetz, T. Yoshioka, and M. Kawato, “Human Cerebellar Activity reflecting an Acquired Internal Model of a New Tool,” Nature, 403, pp. 192-195, 2000.
  17. [17] A. Nambu, “A new dynamic model of the cortico-basal ganglia loop,” Progress in Brain Research, 143, pp. 461-466, 2004.
  18. [18] H. Imamizu, T. Kuroda, S. Miyauchi, T. Yoshioka, and M. Kawato, “Modular Organization of Internal Models of Tools in the Human Cerebellum,” Proc. Natl. Acad. Sci. USA., 100, pp. 5461-5466, 2003.
  19. [19] D. M. Wolpert and J. R. Flanagan, “Motor Prediction,” Current Biology, 11(18), pp. 729-732, 2001.
  20. [20] J. Izawa, T. Kondo, and K. Ito, “Biological arm motion through reinforcement learning,” Biological Cybernetics, Vol.91 (1), pp. 10-22, 2004.
  21. [21] J. Izawa, T. Kondo, and K. Ito, “Motor learning model using reinforcement learning with neural internal model,” Proc. of International conference on robotice & automation (ICRA), pp. 3146-3151, 2003.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 05, 2021