A Neural Model for Exploration and Learning of Embodied Movement Patterns

Kaito Kinjo; Cota Nabeshima; Shinji Sangawa; Yasuo Kuniyoshi

doi:10.20965/jrm.2008.p0358

single-rb.php

« previous

JRM Vol.20 No.3 pp. 358-366

doi: 10.20965/jrm.2008.p0358

(2008)

Paper:

Views over last 60 days: 498

A Neural Model for Exploration and Learning of Embodied Movement Patterns

Kaito Kinjo, Cota Nabeshima, Shinji Sangawa, and Yasuo Kuniyoshi

The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan

Received:

September 29, 2007

Accepted:

January 10, 2008

Published:

June 20, 2008

Keywords:

neural model, cortico-spinal-musculo model, self-organizing primary motor area, embodiment, autonomous exploration and learning

Abstract

Despite increased interest in the study of human motor development among researchers ranging brain scientists to roboticists, many aspects of the mechanisms involved remain to be clarified. Talking a synthetic approach to development, to extract the property of the mechanism for a new robot controller, we propose two movement learning properties in the human being: (1) compression of redundant motor commands and (2) mapping from sensors to motors in the coupling of the controller, the body, and the environment. To prove the feasibility of our proposition, we constructed a neural model having essential biological features. In a series of experiments with a simple body model, rhythmic movement (to be learned in early infancy) is explored and correctly learned; moreover entrainment is observed. Our results suggest that our model can learn rhythmic movement, confirming a first step towards understanding of human development.

Cite this article as:

K. Kinjo, C. Nabeshima, S. Sangawa, and Y. Kuniyoshi, “A Neural Model for Exploration and Learning of Embodied Movement Patterns,” J. Robot. Mechatron., Vol.20 No.3, pp. 358-366, 2008.

Data files:

References

[1] H. I. M. Asada, K.F.MacDorman, and Y. Kuniyoshi, “Cognitive developmental robotics as a new paradigm for the design of humanoid robots,” Robotics and Autonomous sys., 37, pp. 185-193, 2001.
[2] Y. Kuniyoshi and S. Sangawa, “Early motor development from partially ordered neural-body dynamics, experiments with a corticospinal-musculo-sleletal model,” Biol. Cybern, 95, pp. 589-605, 2006.
[3] R. O. S. Schaal, D. Sternad, and M. Kawato, “Rhythmic arm movement is not discrete,” Nature Neurosci., 7, pp. 1136-1143, 2004.
[4] M. L. M. Small, K. Judd, and S. Stick, “Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep,” J. of Applied Physiol., 86, pp. 359-376, 1999.
[5] R. T. G. Taga and T. Konishi, “Analysis of General Movements towards Understanding of Developmental Principle,” In Proc. of IEEE SMC, pp. V678-683, 1999.
[6] W. Penfield and E. Boldrey, “Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation,” Brain, 60, pp. 389-443, 1937.
[7] Y. Kuniyoshi and S. Suzuki, “Dynamic emergence and adaptation of behavior through embodiment as coupled chaotic field,” In Proc. of IEEE IROS, pp. 2042-2049, 2004.
[8] C. Phillips and R. Porter, “Corticospinal neurones Their role in movement,” Academic Press, London, 1977.
[9] M. Kawato and H. Gomi, “The cerebellum and VOR/OKR learning models,” Biol. Cybern, 68, pp. 95-103, 1992.
[10] M. S. M. Ito and P. Tongroach, “Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebelar Purkinje cells,” J. of Physiol, 324, pp. 113-134, 1982.
[11] H. Head and G. Holmes, “Sensory disturbances from cerebral lesions,” Brain, 34, pp. 102-245, 1911.
[12] T. Kohonen, “Self-Organizing Maps,” Springer, 1996.
[13] T. Sejnowski, “Storing covariance with nonlinearly interacting neurons,” J. of Math. Biol., 4, pp. 303-321, 1977.
[14] G. H. D.E. Rumelhart and R. Williams, “Learning representaions by back-propagating errors,” Nature, 323-9, 533-536, 1986.
[15] R. Smith, “Open Dynamics Engine - ODE,”
http://www.ode.org/.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] H. I. M. Asada, K.F.MacDorman, and Y. Kuniyoshi, “Cognitive developmental robotics as a new paradigm for the design of humanoid robots,” Robotics and Autonomous sys., 37, pp. 185-193, 2001.

[2] [2] Y. Kuniyoshi and S. Sangawa, “Early motor development from partially ordered neural-body dynamics, experiments with a corticospinal-musculo-sleletal model,” Biol. Cybern, 95, pp. 589-605, 2006.

[3] [3] R. O. S. Schaal, D. Sternad, and M. Kawato, “Rhythmic arm movement is not discrete,” Nature Neurosci., 7, pp. 1136-1143, 2004.

[4] [4] M. L. M. Small, K. Judd, and S. Stick, “Is breathing in infants chaotic? Dimension estimates for respiratory patterns during quiet sleep,” J. of Applied Physiol., 86, pp. 359-376, 1999.

[5] [5] R. T. G. Taga and T. Konishi, “Analysis of General Movements towards Understanding of Developmental Principle,” In Proc. of IEEE SMC, pp. V678-683, 1999.

[6] [6] W. Penfield and E. Boldrey, “Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation,” Brain, 60, pp. 389-443, 1937.

[7] [7] Y. Kuniyoshi and S. Suzuki, “Dynamic emergence and adaptation of behavior through embodiment as coupled chaotic field,” In Proc. of IEEE IROS, pp. 2042-2049, 2004.

[8] [8] C. Phillips and R. Porter, “Corticospinal neurones Their role in movement,” Academic Press, London, 1977.

[9] [9] M. Kawato and H. Gomi, “The cerebellum and VOR/OKR learning models,” Biol. Cybern, 68, pp. 95-103, 1992.

[10] [10] M. S. M. Ito and P. Tongroach, “Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebelar Purkinje cells,” J. of Physiol, 324, pp. 113-134, 1982.

[11] [11] H. Head and G. Holmes, “Sensory disturbances from cerebral lesions,” Brain, 34, pp. 102-245, 1911.

[12] [12] T. Kohonen, “Self-Organizing Maps,” Springer, 1996.

[13] [13] T. Sejnowski, “Storing covariance with nonlinearly interacting neurons,” J. of Math. Biol., 4, pp. 303-321, 1977.

[14] [14] G. H. D.E. Rumelhart and R. Williams, “Learning representaions by back-propagating errors,” Nature, 323-9, 533-536, 1986.

[15] [15] R. Smith, “Open Dynamics Engine - ODE,”
http://www.ode.org/.