Synthesis of robot's behaviors from few examples

This paper addresses the problem of the acquisition of robot’s behaviors for real environments. It insists on the interest of learning behaviors during robot’s interaction with the environment under the control of a human tutor. The paper presents a learning model conceived to synthesize behaviors from a set of few examples and 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.