Skip to main content
SpringerLink
Log in

A model for sensorimotor control and learning

  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

A model for motor learning, generalization, and adaptation is presented. It is shown that the equations of motion of a limb can be expressed in a parametric form that facilitates transformation of desired trajectories into plans. These parametric equations are used in conjunction with a quantized multidimensional memory organized by state variables. The memory is supplied with data derived from the analysis of practice movements. A small computer and mechanical arm are used to implement the model and study its properties. Results verify the ability to acquire new movements, adapt to mechanical loads, and generalize between similar movements.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Albert,A.: Regression and the Moore-Penrose pseudoinverse. New York: Academic Press 1972

    Google Scholar 

  • Bryson,A.E., Ho,Y.C.: Applied optimal control. Waltham: Ginn 1969

    Google Scholar 

  • Eden,M.: Handwriting and pattern recognition. IRE trans. Inf. Theory, IT-8, 160–166 (1962)

    Google Scholar 

  • Evarts,E.V., Bizzi,E., Burke,R.E., Delong,M., Thach,W.T.,Jr.: Central control of movement. Neurosc. Res. Prog. Bull. 9 (1970)

  • Fu,K.S.: Learning control systems and intelligent control systems: an intersection of artificial intelligence and automatic control. IEEE trans. Auto. Contr., 70–72 (1971)

  • Gonshor,A., Melvill Jones,G.: Extreme vestibulo-ocular adaptation induced by prolonged optical reversal of vision. J. Physiol. 256, 361–379 (1976)

    Google Scholar 

  • Hammond,P.H.: The influence of prior instruction to the subject on an apparently involuntary neuro-muscular response. J. Physiol. 127, 17–18 (1956)

    Google Scholar 

  • Hein,A., Held,R.: A neural model for labile sesorimotor coordination. In: Biological prototypes and synthetic systems, Vol. 1, pp. 71–74. Bernard,E., Kare,M., eds. New York: Plenum Press 1962

    Google Scholar 

  • Held,R.: Exposure-history as a factor in maintaining stability of perception and coordination. J. Nerv. Ment. Dis. 132, 26–32 (1961)

    Google Scholar 

  • Held,R., Hein,A.: Movement-produced stimulation in the development of visually guided behavior. J. Comp. Physiol. Psych. 56, 872–876 (1963)

    Google Scholar 

  • Helmholtz,H.V.: Handbook of physiological optics, Vol. 3. Leipzig: Voss 1867

    Google Scholar 

  • Holst,E.V.: Relations between the central nervous system and the peripheral organs. Brit. J. Anim. Behav. 2, 89–94 (1954)

    Google Scholar 

  • Horn,B.K.P., Inoue,H.: Kinematics of the MIT-AI-Vicarm manipulator. MIT Working Paper 69, May 1974

  • Kahn,M.E.: The near-minimum time control of open-loop articulated kinematic chains. Stanford Artificial Intelligence Memo No. 106, December, 1969

  • Marr,D.: A theory of cerebellar cortex. J. Physiol. 202, 437–470 (1969)

    Google Scholar 

  • Melvill Jones,G., Watt,D.O.D.: Observations on the control of stepping and hopping movements in man. J. Physiol. 219, 709–727 (1971a)

    Google Scholar 

  • Melvill Jones,G., Watt,D.O.D.: Muscular control of landing from unexpected falls in man. J. Physiol. 219, 729–737 (1971b)

    Google Scholar 

  • Miles,F., Fuller,J.H.: Adaptive plasticity in the vestibulo-ocular responses of the rhesus monkey. Brain Res. 80, 512–516 (1974)

    Google Scholar 

  • Mittelstaedt,H.: The analysis of behavior in terms of control systems. In: Group processes, transactions of the fifth conference, pp. 45–84. Schaffner, B., ed. New Jersey: Princeton 1958

    Google Scholar 

  • Paul,R.: Modelling trajectory calculation and servoing of a computer controlled arm. Stanford Artificial Intelligence Memo No. 177, November, 1972

  • Raibert,M.H.: A state space model for sensorimotor control and learning. MIT Artificial Intelligence Memo No. 351, January, 1976

  • Raibert,M.H.: Control and learning by the State Space Model: experimental findings. MIT Artificial Intelligence Memo No. 412, March, 1977

  • Raibert,M.H.: Analytical equations vs. table look-up for manipulation: a unifying concept. To be presented at IEEE Conference on Decision and Control, 1977

  • Rust,B., Burrus,W.R., Schneeberger,C.: A simple algorithm for computing the generalized inverse of a matrix. Commun. ACM 9, 381–387 (1966)

    Google Scholar 

  • Tsypkin,Y.Z.: Adaptation and learning in automatic systems. (Translated by Nikoloic, Z.J.) New York: Academic Press 1971

    Google Scholar 

  • Waters,R.C.: A mechanical arm control system. MIT Artificial Intelligence Memo No. 301, January, 1974

  • White,B.L.: Experience and the development of motor mechanisms in infancy. In: Mechanisms of motor skill development, pp. 95–132. Conolly, K., ed. London: Academic Press, 1970

    Google Scholar 

  • Young,L.R., Stark,L.: Biological control systems — a critical review and evaluation: developments in manual control. NASA, Cr-190, 1965

Download references

Author information

Authors and Affiliations

  1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    M. H. Raibert

Authors
  1. M. H. Raibert
    View author publications

    You can also search for this author in PubMed Google Scholar

Additional information

This research was done while the author was a graduate student at the Massachusetts Institute of Technology in the Artificial Intelligence Laboratory and Department of Psychology. It was supported in part by training grant NGMS 5-T01-GM01064-15

Rights and permissions

Reprints and permissions

About this article

Cite this article

Raibert, M.H. A model for sensorimotor control and learning. Biol. Cybernetics 29, 29–36 (1978). https://doi.org/10.1007/BF00365233

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00365233

Keywords

Search

Navigation