A New Neural Net Approach to Robot 3D Perception and Visuo-Motor Coordination

  • Sukhan Lee
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 202)


This paper presents a new neural net approach to robot visuo-motor coordination. The approach, refered to here as “neurobotics”, establishes neural net-based visual error servoing as a fundamental mechanism of implementing visuo-motor coordination. Visual error servoing is achieved by projecting the robot task space on the 3D perception net (representing the robot internal 3D space) and generating an incremental change of 3D space arm configuration in the 3D perception net based on potential field-based reactive path planning. The 3D space arm configuration planned in the 3D perception net is then translated into the corresponding joint angles through the arm kinematic net. The arm kinematic net is formed based on hierarchically self-organizing competitive and cooperative network. Simulation results are shown.


Hide Unit Index Cell Stereo Match Cooperative Network Robot Hand 
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. [1]
    G. Carpenter and S. Grossberg. Art2: stable self-organization of pattern recognition codes for analog input patterns. Applied Optics, 26:4919–4930, 1987.CrossRefGoogle Scholar
  2. [2]
    R. Eckmiller Neural networks for generation of eye and arm movement trajectories. In M. Ito, editor, Neural Programming, pages 173–187. Karger: Basel, 1989.Google Scholar
  3. [3]
    M. Kuperstein. Adaptive visual-motor coordination in multijoint robots using parallel architecture. In Proc. IEEE Int. Conf. Automat. Robotics, 1987. (Raleigh, NC).Google Scholar
  4. [4]
    M. Kuperstein. Infant neural controller for adaptive sensory-motor coordination. Neural Networks, 4:131–145, 1991.CrossRefGoogle Scholar
  5. [5]
    S. Lee and R. M. Kil. A gaussian potential network with hierarchically self-organizing learning. Neural Networks, 4:207–224, 1991.CrossRefGoogle Scholar
  6. [6]
    D. Marr and T. Poggio. Cooperative computation of stereo disparity. SCIENCE, 194:283–287, 1976.CrossRefGoogle Scholar
  7. [7]
    B. W. Mel. Connectionist robot motion planning. Academic Press, 1990.MATHGoogle Scholar
  8. [8]
    H. J. Ritter T. M. Martinetz and K. J. Schulten. Three-dimensional neural net for learning visuomotor coordination of a robot arm. IEEE Trans. Neural Net., 1:131–136,1990.CrossRefGoogle Scholar
  9. [9]
    Y. T. Zhou and R. Chellappa. Neural network algorithms for motion stereo. In Proc. Int. Joint Conf. Neural Networks, pages 11:251–258, 1989. (Washington D.C.).CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1993

Authors and Affiliations

  • Sukhan Lee
    • 1
    • 2
  1. 1.Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA
  2. 2.Dept. of EE-SystemsUniversity of Southern CaliforniaUSA

Personalised recommendations