Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems

This paper describes how agents can learn an internal model of the world structurally by focusing on the problem of behavior-based articulation. We develop an on-line learning scheme—the so-called mixture of recurrent neural net (RNN) experts—in which a set of RNN modules become self-organized as experts on multiple levels, in order to account for the different categories of sensory-motor flow which the robot experiences. Autonomous switching of activated modules in the lower level actually represents the articulation of the sensory-motor flow. In the meantime, a set of RNNs in the higher level competes to learn the sequences of module switching in the lower level, by which articulation at a further, more abstract level can be achieved. The proposed scheme was examined through simulation experiments involving the navigation learning problem. Our dynamical system analysis clarified the mechanism of the articulation. The possible correspondence between the articulation mechanism and the attention switching mechanism in thalamo-cortical loops is also discussed.