Perception: Insights from the sensori-motor approach

A wide variety of visual recognition systems are developed for precise tasks and types of objects. In this paper we would like to emphasize ways to build a more generic recognition system. Perception is one of these mechanisms that psychologists particularly pointed out as a fundamental one for actively organizing and making sense of input sensory information. Based on psychological assumptions, we propose to explore the concept of perception, infer formalization in the dynamical system framework and quantitatively analyze it on robotic platforms using a unique simple neuronal architecture based on the association of visual and motor information (movements of the body or part of the body). This coupling of sensory flows of information can be characterized by a sensorimotor invariant, a dynamical attractor that we identify as a perception function. For place, object or facial expression recognition, we show how simple sensori-motor architecture can be applied to accomplish each task in terms of behavioral recognition. In each application, some pertinent visual information, based on classical focus point detection, are organized as local views and associated to an action or an internal state corresponding to a set of actions, in order to reach a location, an object or recognize a facial expression. The active learning phase for different points of view or face expressions allows the emergence of a stable perception linked to a stable sensori-motor attractor and allows the robot to perform a stable behavior in very different initial conditions. We will show how the attractor/perception emerges during the learning phase and evaluate its spatial generalization properties.