Abstract

Subjective appreciation and performance evaluation
of a robot by users are two important dimensions for Human- Robot Interaction, especially as increasing numbers of people become involved with robots. As roboticists we have to carefully design robots to make the interaction as smooth and enjoyable as possible for the users, while maintaining good performance in the task assigned to the robot. In this paper, we examine the impact of providing a robot with learning capabilities on how users report the quality of the interaction in relation to objective performance. We show that humans tend to prefer interacting with a learning robot and will rate its capabilities higher even if the actual performance in the task was lower. We suggest that adding learning to a robot could reduce the apparent load felt by a user for a new task and improve the user’s evaluation of the system, thus facilitating the integration of such robots into existing work flows