Teaching a Robot with Human Natural Movements

  • Giovanni Magenes
  • Emanuele Secco
Part of the CISM Courses and Lectures book series (CISM, volume 473)


A human inspired sensorimotor approach to robotic systems could solve the problem of designing adaptive and learning robot for widely versatile tasks. We present a neural network controller for driving a 3 degree of freedom mechanical robotic finger to a desired position in space. The controller has been taught with human movements of the corresponding finger. At the end of the training process, it was able to closely imitate the physiological control and the motion planning strategy of the human beings. Generalization properties are shown after the training process (that is the capacity to move the device in different directions and places never seen during the teaching phase). The approach seems promising for controlling artificial prosthetic and robotic upper limbs in general.


Kinematic Model Humanoid Robot Multi Layer Perceptron Real Movement Natural Movement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Brooks, R.A., and Stein, L.A. (1994). Building brains for bodies. Autonomous Robots 1(1):7–25.CrossRefGoogle Scholar
  2. Flash, T., and Hogan, N. (1985). The coordination of arm movements: an experimentally confirmed mathematical model. Journal of Neuroscience 5:1688–1703.Google Scholar
  3. Secco, E.L., and Magenes, G. (2002). A feedforward neural network controlling the movement of a 3 degree of freedom finger, IEEE SMC Transaction Part A 32(3):437–445.Google Scholar
  4. Secco, E.L., Visioli, A., and Magenes, G. (2002). Minimum jerk motion planning for a prosthetic finger, Journal of Robotic Systems (submitted).Google Scholar
  5. Da Cunha, F.L., Schneebeli, H. A. and Dynnikov V. I., (2000). Development of anthropomorphic upper limb prostheses with human-like interphalangian and interdigital couplings. Artificial Organs 24(3):193–197.CrossRefGoogle Scholar
  6. Haykin, S. (1999). Neural Networks — A Comprehensive Foundation, Second Edition, Prentice HallzbMATHGoogle Scholar
  7. Vicini, O. (2003). Controllo neurale di un dito robotico allenato tramite movimenti di un dito umano, Thesis, Tutor Magenes, G., Dip. Informatica e Sistemistica, University of Pavia, Pavia — ItalyGoogle Scholar
  8. Caiti, A., Canepa, G., De Rossi, D., Germagnoli, F., Magenes, G., Parisini, T. (1995). Towards the realization of an artificial tactile system: fine-form discrimination by a tensorial tactile sensor array and neural inversion algorithms. IEEE SMC Transaction 25(6):933–946.Google Scholar
  9. Magenes, G., Ramat, S., Secco, E.L. (2002). Life-like sensorimotor control: from biological systems to artifacts. Current Psychology of Cognition 21(4–5): 565–596.Google Scholar

Copyright information

© Springer-Verlag Wien 2004

Authors and Affiliations

  • Giovanni Magenes
    • 1
  • Emanuele Secco
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversity of PaviaPaviaItaly

Personalised recommendations