TCB Publications - Abstract

Thomas Martinetz and Klaus Schulten. Hierarchical neural net for learning control of a robot's arm and gripper. In International Joint Conference on Neural Networks, San Diego, California, volume 2, pp. 747-752. The Institute of Electrical and Electronics Engineers, New York, 1990.

MART90B We introduce a hierarchical neural network structure capable of learning the control of a robot's arm and gripper. Based on Kohonen's algorithm 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-dimensional cubic lattice in which is nested at each lattice node a 2-dimensional square lattice. The robot learns without supervision to position its arm and to properly orient its gripper by observing its own trial movements. In our simulation, the error in positioning the manipulator after training was 0.3

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