Describes an intelligent control architecture designed to endow human-like capabilities to robots and report experimental results that demonstrate the utility of this architecture in controlling a redundant dynamic manipulator in a hybrid position/force control task. Motor synergies that arise when control of a subset of the available degrees of freedom is coupled and coordinated to accomplish specific task sub-goals are used to simplify the problem, of controlling redundant systems by reducing the dimensionality of the control space. Using synergies as a basis control set gives the controller the general ability to execute novel tasks in unstructured environments. In addition, the rapid learning capabilities of the controller permit refinement of control through the acquisition of skilled control with practice.

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.>