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Integrating neural networks and knowledge-based systems for robotic control

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3 Author(s)
Handelman, David A. ; Dept. of Psychol., Princeton Univ., NJ, USA ; Lane, Stephen H. ; Gelfand, Jack J.

The authors address the issue of integrating both computational paradigms for the purpose of robotic manipulation. The control task chosen to demonstrate the integration technique involves teaching a two-link manipulator how to make a tennis-like swing. A three-level task hierarchy is defined consisting of low-level reflexes, reflex modulators, and an execution monitor. The rule-based execution monitor first determines how to make a successful swing using rules alone. It then teaches a neural network how to accomplish the task by having it observe rule-based task execution. Following initial training, the execution monitor continuously evaluates neural network performance and re-engages swing-maneuver rules whenever changes in the manipulator or its operating environment necessitate retraining of the network. Simulation results show the interaction between rule-based and network-based system components during various phases of training and supervision

Published in:

Robotics and Automation, 1989. Proceedings., 1989 IEEE International Conference on

Date of Conference:

14-19 May 1989