A neural network with Hebbian-like adaptation rules learning visuomotor coordination of a PUMA robot

A hybrid neural network algorithm which employs superpositions of linear mappings is presented. The algorithm´s application to the task of learning the end effector positioning of a robot arm is described. The learning and the control of the positioning is accomplished by the network solely through visual input from a pair of cameras. In addition to the learning of the a priori unknown input-output relation from target locations seen by the cameras to corresponding joint angles, the network provides the robot with the ability to perform feedback-guided corrective movements. This allows the positioning movement to be divided into an initial, open-loop controlled positioning and subsequent feedback-guided corrections. For the robot arm employed, the neural network algorithm achieves final positioning error of about 1.3 mm, the lower bound given by the finite resolution of the cameras