Computational Analysis of Motionese Toward Scaffolding Robot Action Learning

A difficulty in robot action learning is that robots do not know where to attend when observing action demonstration. Inspired by human parent-infant interaction, we suggest that parental action demonstration to infants, called motionese, can scaffold robot learning as well as infants´. Since infants´ knowledge about the context is limited, which is comparable to robots, parents are supposed to properly guide their attention by emphasizing the important aspects of the action. Our analysis employing a bottom-up attention model revealed that motionese has the effects of highlighting the initial and final states of the action, indicating significant state changes in it, and underlining the properties of objects used in the action. Suppression and addition of parents´ body movement and their frequent social signals to infants produced these effects. Our findings are discussed toward designing robots that can take advantage of parental teaching.