Synthesis of robot´s behaviors from few examples

This paper addresses the problem of the acquisition of robot´s behaviors for real environments. It refers to the learning behaviors during robot´s interaction with the environment under the control of a human tutor. The paper presents the learning model conceived to synthesize behaviors from a set of few examples, relying on a distributed representation of perception/action relations. The model is experienced on a real robot to learn a slalom task without giving any a priori information about the task or any element of the environment. The model exhibits properties that are well adapted to the interactive learning of concrete behaviors.