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In this paper, we investigate a sensory-motor architecture allowing a robot to learn to recognize postures. The learning is performed without a teaching signal that associates a specific posture with the robot's motor internal state. Our architecture assumes that the robot initially performs postures, then the human imitates them. An on-line learning scheme without an explicit reward or ad-hoc detection mechanism or a formatted teaching technique is proposed. Investigations on how a “naive” system can learn to imitate correctly another person's posture during a natural interaction motivate the current research work.

In this work, we are interested in understanding how emotional interactions with a social partner can bootstrap increasingly complex behaviors such as social referencing. Our idea is that social referencing, facial expression recognition and the joint attention can emerge from a simple sensori-motor architecture. Without knowing that the other is an agent, we show our robot is able to learn some complex tasks if the human partner has a low level emotional resonance with the robot head. Hence we advocate the idea that social referencing can be bootstrapped from a simple sensori-motor system not dedicated to social interactions.

International audience; We are interested in understanding how babies learn to recognize facial expressions without having a teaching signal allowing to associate a facial expression to a given abstract label (i.e the name of the facial expression 'sadness', 'happiness'...). Our starting point was a mathematical model showing that if the baby uses a sensory motor architecture for the recognition of the facial expression then the parents must imitate the baby facial expression to allow the on-line learning. In this paper, a first series of robotics experiments showing that a simple neural network model can control the robot head and learn on-line to recognize the facial expressions (the human partner imitates the robot prototypical facial expressions) is presented. We emphasize the importance of the emotions as a mechanism to ensure the dynamical coupling between individuals allowing to learn more complex tasks.

International audience; 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.