Abstract

Child-robot interactions are increasingly being explored in domains which require longer-term application, such as healthcare and education. In order for a robot to behave in an appropriate manner over longer timescales, its behaviours should be coterminous with that of the interacting children. Generating such sustained and engaging social behaviours is an on-going research challenge, and we argue here that the recent progress of deep machine learning opens new perspectives that the HRI community should embrace. As an initial step in that direction, we propose the creation of a large open dataset of child-robot social interactions. We detail our proposed methodology for data acquisition: children interact with a robot puppeted by an expert adult during a range of playful face-to- face social tasks. By doing so, we seek to capture a rich set of human-like behaviours occurring in natural social interactions, that are explicitly mapped to the robot's embodiment and affordances.