Posture recognition analysis during human-robot imitation learning

Imitation is a powerful paradigm for learning new actions and tasks and is currently investigated in several research domains such as developmental psychology or robotics [8]. For example, learning by demonstration is employed in robotics to teach new skills and tasks to robots [1] [2]. The dynamics of coupling of perception of one partner to the action of the other, and vice-and-versa, has been exploited to characterize social traits [9]. In particular, in the case of strong coupling such as imitation, research in developmental science has shown that imitation could be considered a social referencing mechanism [7]. Recently, identification of social traits during human-robot imitation learning has been successfully demonstrated. For example a social signature emerges from a neural network architecture and distinguishes typical children and children with Autism Spectrum Disorders (ASD). The key idea of this approach is to learn the mapping between the perception of postures performed by the children and the actions performed by the robot. A perception-action architecture based on neural network (PerAc [6]) is used to associate the perception with action [3]. Interestingly, this approach generalizes to person's identity recognition by the analysis of the dynamics of the social signature (novelty detection procedure) [5].