Adaptive learning with the growing competitive linear local mapping network for robotic hand-eye coordination

Traditionally, linear local mapping networks learn the entire workspace, and the neurons are placed according to the Kohonen map or its variant, the “neural gas”. In this paper a new neural network is introduced, which allocates neurons adaptively following the current trajectory, according to an error criterion. The resulting network has a small number of neurons and is thus very efficient. It also learns very quickly: employing active learning and the RLS algorithm, just one pass is sufficient for our algorithm to acquire the Jacobians that are needed to perform a given positioning of the robot´s gripper on the target. Also, an online adaptation of the Jacobians is proposed