Neural networks based autonomous learning for a desktop robot

A method of realizing desktop robot´s negative phototaxis through a neural network is presented. The biology is characteristic of biologic phototaxis and negative phototaxis. Can a machine be endowed with such a characteristic? This is the question we study in this paper. A randomly generated network is used as the main computational unit. Only the weights of the output units from this network are adjusted during the training phase. Learning samples are collected according to the energy function. It will be shown that this simple type of a biological realistic neural network is able to simulate robot controllers like that incorporated in desktop robots. The experiments are presented illustrating the stage-like study emerging with this learning mode.