Integrating neural networks and knowledge-based systems for intelligent robotic control

A methodology is presented for integrating artificial neural networks and knowledge-based systems for the purpose of robotic control. The integration is patterned after models of human motor skill acquisition. The initial control task chosen to demonstrate the integration technique involves teaching a two-link manipulator how to make a specific type of 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 cerebellar model articulation controller (CMAC) neural networks how to accomplish the task by having them 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 networks. Simulation results show the interaction between rule-based and network-based system components during various phases of training and supervision.<
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