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.

We present a practical method for autonomous synthesis of appropriate admittance behavior for robust high-precision robotic assembly. Because our approach relies on online learning of the appropriate admittance through repeated attempts at the assembly operation, we are able to circumvent the problems alternative approaches have in trying to model the interactions between the robot and its environment. Test results on the peg-in-hole insertion task show that the performance of our approach compares favorably with that of other methods recently proposed for high-precision chamferless peg-in-hole insertion.>

A peg-in-hole insertion task is used as an example to illustrate the utility of direct associative reinforcement learning methods for learning control under real-world conditions of uncertainty and noise. An associative reinforcement learning system has to learn appropriate actions in various situations through a search guided by evaluative performance feedback The authors used such a learning system, implemented as a connectionist network, to learn active compliant control for peg-in-hole insertion. The results indicated that direct reinforcement learning can be used to learn a reactive control strategy that works well even in the presence of a high degree of noise and uncertainty.>