Designing and Evaluating an Adaptive Spoken Dialogue System
User Modeling and User-Adapted Interaction
Towards developing general models of usability with PARADISE
Natural Language Engineering
Whose job is it anyway? a study of human-robot interaction in a collaborative task
Human-Computer Interaction
Evaluating description and reference strategies in a cooperative human-robot dialogue system
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Situated reference in a hybrid human-robot interaction system
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Social interactions in HRI: the robot view
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Studies on grounding with gaze and pointing gestures in human-robot-interaction
ICSR'12 Proceedings of the 4th international conference on Social Robotics
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Humans take different roles when they work together on a common task. But how do humans react to different roles of a robot in a human-robot interaction scenario? In this publication, we present a user evaluation, in which naïve participants work together with a robot on a common construction task. The robot is able to take different roles in the interaction: one group of the experiment participants worked with the robot in the instructive role, in which the robot first instructs the user how to proceed with the construction and then supports the user by handing over building pieces. The other group of participants used the robot in its supportive role, in which the robot hands over assembly pieces to the human that fit to the current progress of the assembly plan and only gives instructions when necessary. The results of the experiment show that the users do not prefer one of the two roles of the robot, but take the counterpart to the robot's role and adjust their own behaviour according to the robot's actions. This is revealed by the objective data that we collected as well as by the subjective answers of the experiment participants to a user questionnaire. The data suggests that the most influential factors for user satisfaction are the number of times the users picked up a building piece without getting an explicit instruction by the robot and the number of utterances the users made themselves. While the number of pickup actions had a positive or negative influence, depending on the role the users took, the number of own utterances always had a strong negative influence on the user's satisfaction.