Children's knowledge and expectations about robots: A survey for future user-centered design of social robots

This paper seeks to establish a precedent for future development and design of social robots by considering the knowledge and expectations about robots of a group of 296 children. Humanrobot interaction experiments were conducted with a Teleoperated anthropomorphic robot, and surveys were taken before and after the experiments. Children were also asked to perform a drawing of a robot. An image analysis algorithm was developed to classify drawings into 4 types: Anthropomorphic Mechanic/Non Mechanic (AM/AnM) and Non-Anthropomorphic Mechanic/Non Mechanic (nAM/nAnM). Image analysis algorithm was used in combination with human classification using a 2003 (two out of three) voting scheme to find children's strongest stereo-type about robots. Survey and image analysis results suggest that children in general have some general knowledge about robots, and some children even have a deep understanding and expectations for future robots. Moreover, children's strongest stereotype is directed towards mechanical anthropomorphic systems.