Acquisition of qualitative representations for learning robots

Learning robots are faced with two major issues: identification of the dynamics of the robot and identification of the environment as well as its interaction with the robot. We discuss in this paper a way to acquire representations of both these concepts through an iterative learning procedure. Furthermore we will concentrate on qualitative representations, since we are not necessarily interested in the precise equations of the dynamics or the exact location of potential limits of the viability domain. A more "fuzzy" knowledge can be sufficient to guarantee a satisfactory control of a robot. The key notion of our representations is the phase space: it will be used to express the dynamics of the controlled robot, as well as the landmark values, corresponding to the environment, that refer either to control goals or obstacles to be avoided. The important issue is that the exposed learning procedure provides a way to acquire these various knowledges.