Learning Bayesian models of sensorimotor interaction: from random exploration toward the discovery of new behaviors

We are interested in probabilistic models of space and navigation. We describe an experiment where a Koala robot uses experimental data, gathered by randomly exploring the sensorimotor space, so as to learn a model of its interaction with the environment. This model is then used to generate a variety of new behaviors, from obstacle avoidance to wall following to ball pushing, which were previously unknown by the robot. The learned model can be seen as a building block for a hierarchical control architecture based on the Bayesian map formalism.