Parental scaffolding is an important mechanism that speeds up infant sensorimotor development. Infants pay stronger attention to the features of the objects highlighted by parents, and their manipulation skills develop earlier than they would in isolation due to caregivers' support. Parents are known to make modifications in infant-directed actions, which are often called “motionese”7. The features that might be associated with motionese are amplification, repetition and simplification in caregivers' movements, which are often accompanied by increased social signalling. In this paper, we extend our previously developed affordances learning framework to enable our hand-arm robot equipped with a range camera to benefit from parental scaffolding and motionese. We first present our results on how parental scaffolding can be used to guide the robot learning and to modify its crude action execution to speed up the learning of complex skills. For this purpose, an interactive human caregiver-infant scenario was realized with our robotic setup. This setup allowed the caregiver's modification of the ongoing reach and grasp movement of the robot via physical interaction. This enabled the caregiver to make the robot grasp the target object, which in turn could be used by the robot to learn the grasping skill. In addition to this, we also show how parental scaffolding can be used in speeding up imitation learning. We present the details of our work that takes the robot beyond simple goal-level imitation, making it a better imitator with the help of motionese.

This paper addresses the problem of self-detection by a robot. The paper describes a methodology for autonomous learning of the characteristic delay between motor commands (efferent signals) and observed movements of visual stimuli (afferent signals). The robot estimates its own efferent-afferent delay from self-observation data gathered while performing motor babbling, i.e., random rhythmic movements similar to the primary circular reactions described by Piaget. After the efferent-afferent delay is estimated, the robot imprints on that delay and can later use it to successfully classify visual stimuli as either “self” or “other.” Results from robot experiments performed in environments with increasing degrees of difficulty are reported.