This paper presents two studies that investigate how people praise and punish robots in a collaborative game scenario. In a first study, subjects played a game together with humans, computers, and anthropomorphic and zoomorphic robots. The different partners and the game itself were presented on a computer screen. Results showed that praise and punishment were used the same way for computer and human partners. Yet robots, which are essentially computers with a different embodiment, were treated differently. Very machine-like robots were treated just like the computer and the human; robots very high on anthropomorphism / zoomorphism were praised more and punished less. However, barely any of the participants believed that they actually played together with a robot. After this first study, we refined the method and also tested if the presence of a real robot, in comparison to a screen representation, would influence the measurements. The robot, in the form of an AIBO, would either be present in the room or only be represented on the participants’ computer screen (presence). Furthermore, the robot would either make 20% errors or 40% errors (error rate) in the collaborative game. We automatically measured the praising and punishing behavior of the participants towards the robot and also asked the participant to estimate their own behavior. Results show that even the presence of the robot in the room did not convince all participants that they played together with the robot. To gain full insight into this human–robot relationship it might be necessary to directly interact with the robot. The participants unconsciously praised AIBO more than the human partner, but punished it just as much. Robots that adapt to the users’ behavior should therefore pay extra attention to the users’ praises, compared to their punishments.
Generalization-Based Acquisition of Training Data for Motor Primitive Learning by Neural Networks
