Learning multiple arm postures of a real arm robot

Tarcisio Campos, Klaus Schulten

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


Implementation of a self-organizing adaptive algorithm for visuo-motor control of a real arm robot (Rubbertuator) is reported. The algorithm supports the learning of multiple postures of the redundant arm, reproducing all possible postures for any arbitrary end-effector position of the arm in the three-dimensional workspace. The computational algorithm accomplishes three different artificial neural network components, responsible for visual and motor maps. These components are interconnected, forming a synergetic neural structure where the arm postures are memorized. Learning multiple arm postures is essential to guide the arm end-effector in its workspace. Positioning error of the robot's end-effector is discussed.

Original languageEnglish (US)
Title of host publicationIntelligent Engineering Systems Through Artificial Neural Networks
EditorsC.H. Dagli, L.I. Burke, Y.C. Shin
Number of pages6
StatePublished - 1992
EventProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92 - St.Louis, MO, USA
Duration: Nov 15 1992Nov 18 1992


OtherProceedings of the 1992 Artificial Neural Networks in Engineering, ANNIE'92
CitySt.Louis, MO, USA

ASJC Scopus subject areas

  • Software


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