Structure from behavior in autonomous agents

We describe a learning algorithm that generates behaviors by self-organization of sensorimotor loops in an autonomous robot. The behavior of the robot is analyzed by a multi-expert architecture, where a number of controllers compete for the data from the physical robot. Each expert stabilizes the representation of the acquired sensorimotor mapping in dependence of the achieved prediction error and forms eventually a behavioral primitive. The experts provide a discrete representation of the behavioral manifold of the robot and are suited to form building blocks for complex behaviors.