Hierarchical neural net for learning control of a robot's arm and gripper

Thomas M. Martinetz, Klaus J. Schulten

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

Abstract

A hierarchical neural network structure capable of learning the control of a robot's arm and gripper is introduced. Based on T. Kohonen's algorithm (1982) for the formation of topologically correct feature maps and on an extension of the algorithm for learning of output signals, a simulated robot arm system learns the task of grasping a cylinder. The network architecture is that of a 3-D cubic lattice in which is nested at each lattice node a 2-D square lattice. The robot learns without supervision to position its arm and to orient its gripper properly by observing its own trial movements. In a simulation, the error in positioning the manipulator after training was 0.3% of the robot's dimension, and the residual error in orienting the gripper was 3.8°. Due to cooperation between neighboring neurons during the training phase, fewer than two trial movements per neuron were sufficient to learn the required control tasks.

Original languageEnglish (US)
Title of host publicationIJCNN. International Joint Conference on Neural Networks
PublisherPubl by IEEE
Pages747-752
Number of pages6
StatePublished - 1990
Event1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3) - San Diego, CA, USA
Duration: Jun 17 1990Jun 21 1990

Other

Other1990 International Joint Conference on Neural Networks - IJCNN 90 Part 3 (of 3)
CitySan Diego, CA, USA
Period6/17/906/21/90

ASJC Scopus subject areas

  • Engineering(all)

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