Bio-inspired Architecture for Active Sensorimotor Localization

Determining one's position within the environment is a basic feature of spatial behavior and spatial cognition. This task is of inherently sensorimotor nature in that it results from a combination of sensory features and motor actions, where the latter comprise exploratory movements to different positions in the environment. Biological agents achieve this in a robust and effortless fashion, which prompted us to investigate a bio-inspired architecture to study the localization process of an artificial agent which operates in virtual spatial environments. The spatial representation in this architecture is based on sensorimotor features that comprise sensory sensory features as well as motor actions. It is hierarchically organized and its structure can be learned in an unsupervised fashion by an appropriate clustering rule. In addition, the architecture has a temporal belief update mechanism which explicitly utilizes the statistical correlations of actions and locations. The architecture is hybrid in integrating bottom-up processing of sensorimotor features with top-down reasoning which is able to select optimal motor actions based on the principle of maximum information gain. The architecture operates on two sensorimotor levels, a macro-level, which controls the movements of the agent in space, and on a micro-level, which controls its eye movements. As a result, the virtual mobile agent is able to localize itself within an environment using a minimum number of exploratory actions.