Grounding Perception: A Developmental Approach to Sensorimotor Contingencies

To date, no clear formalism for those mechanisms has arisen in the developmental robotics community. We propose predictive modeling [16], [17] as such a computational mechanism to learn sensorimotor contingencies, and thus acquire perceptive skills. In the context of SMCT, predictive models can be autonomously estimated by the agent to capture structure in the way motor commands actively transform sensory inputs, namely sensorimotor contingencies. Predictive modeling allows the incremental acquisition of skills required in developmental robotics, while providing a computational implementation of the concept of sensorimotor contingencies. Our current implementation of the formalism proposed in this paper uses a method to cluster state transition graphs, to discover densely connected subgraphs. Note that similar methods have already been proposed by others, for example in navigation tasks for the segmentation of location data into rooms [18], or for sub-goal discovery in hierarchical reinforcement learning (e.g.