Abstract
This paper focusses on the intersection of the area of robot control and connectionism approach, and represents an attempt to give a report of the basic principles and concepts of connectionism in robotics, with an outline of a number of recent algorithms used in learning control of manipulation robot. A major concern in this paper is the application of neural networks for learning of kinematic and dynamic relations used in robot motion control at the executive hierarchical level, as well as for the problems in sensor-based robot control
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