A Biologically Inspired Model for Coding Sensorimotor Experience Leading to the Development of Pointing Behaviour in a Humanoid Robot

Robots are gaining more attention in the neuroscientific community as means of verifying theoretical models of social skill development. In particular, humanoid robots which resemble dimensions of a child offer a controlled platform to simulate interactions between humans and infants. Such robots equipped with biologically inspired models of social and cognitive skill development might provide invaluable insights into learning mechanisms infants employ when interacting with others. One such mechanism which develops in infancy is the ability to share and direct attention of interacting participants. Pointing behaviour underlies joint attention and is preceded by hand-eye coordination. Here, we attempt to explain how pointing emerges from sensorimotor learning of hand-eye coordination in a humanoid robot. A robot learned joint configurations for different arm postures using random body babbling. Arm joint configurations obtained in babbling experiment were used to train biologically inspired models based on self-organizing maps. We train and analyse models with various map sizes and depending on their configuration relate them to different stages of sensorimotor skill development. Finally, we show that a model based on self-organizing maps implemented on a robotic platform accounts for pointing gestures when a human presents an object out of reach for the robot.