Affordance-Based Object Recognition Using Interactions Obtained from a Utility Maximization Principle

The interaction of biological agents within the real world is based on their abilities and the affordances of the environment. By contrast, the classical view of perception considers only sensory features, as do most object recognition models. Only a few models make use of the information provided by the integration of sensory information as well as possible or executed actions. Neither the relations shaping such an integration nor the methods for using this integrated information in appropriate representations are yet entirely clear. We propose a probabilistic model integrating the two information sources in one system. The recognition process is equipped with an utility maximization principle to obtain optimal interactions with the environment. We compared an affordance-based system to a non-affordance-based one, both relying on the same architecture, in a simulated and a real world scenario.