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.

As they gain expertise in problem solving, people increasingly rely on patterns and spatially oriented reasoning. This paper describes an associative visual‐pattern classifier and the automated acquisition of new, spatially oriented reasoning agents that simulate such behavior. They are incorporated into a multi‐agent game‐learning program whose architecture robustly combines agents with conflicting perspectives. When tested on three games, the visual‐pattern classifier learns meaningful patterns, and the pattern‐based, spatially oriented agents generalized from these patterns are generally correct. The accuracy of the contribution of each of the newly created agents to the decision‐making process is measured against an expert opponent, and a perceptron‐Iike algorithm is used to learn game‐specific weights for these agents. Much of the knowledge encapsulated by the new agents was previously inexpressible in the program's representation and in some cases is not readily deducible from the rules.

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.>