A self-learning robot vision system

Hisato Kobayashi*, Kenko Uchida, Yutaka Matsuzaki

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


The authors propose a self-learning strategy for robot vision systems which are used to identify the position of the target part handled by a robot. They tried to use a neural network as a decision-making system which determines how to move the robot to reach the exact target on the base of the image acquired by the robot eye. The authors taught this function automatically to the neural network. The total system works as follows: (1) a target object is set at a known position, and the position is taught to the system, (2) the robot moves randomly around the target and the neural network learns the relation between the relative positions and images, and (3) after enough learning, the robot can identify the target located at an arbitrary position.

Original languageEnglish
Title of host publication91 IEEE Int Jt Conf Neural Networks IJCNN 91
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
Number of pages6
ISBN (Print)0780302273
Publication statusPublished - 1991
Externally publishedYes
Event1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
Duration: 1991 Nov 181991 Nov 21


Other1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
CitySingapore, Singapore

ASJC Scopus subject areas

  • Engineering(all)


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