Intrinsically Motivated Multimodal Structure Learning

We present a long-term intrinsically motivated structure learning method for modeling transition dynamics during controlled interactions between a robot and semipermanent structures in the world. These structures serve as the basis for a number of possible future tasks defined as Markov Decision Processes (MDPs). We apply a structure learning technique to a multimodal affordance representation that yields a population of forward models for use in planning. We evaluate the approach using experiments on a bimanual mobile manipulator (uBot-6) that show the performance of model acquisition as the number of transition actions increases.