Abstract
We conducted a user study for which we purposefully programmed faulty behavior into a robot's routine. It was our aim to explore if participants rate the faulty robot different from an error-free robot and which reactions people show in interaction with a faulty robot. The study was based on our previous research on robot errors where we detected typical error situations and the resulting social signals of our participants during social human-robot interaction. In contrast to our previous work, where we studied video material in which robot errors occurred unintentionally, in the herein reported user study, we purposefully elicited robot errors to further explore the human interaction partners' social signals following a robot error. Our participants interacted with a human-like NAO, and the robot either performed faulty or free from error. First, the robot asked the participants a set of predefined questions and then it asked them to complete a couple of LEGO building tasks. After the interaction, we asked the participants to rate the robot's anthropomorphism, likability, and perceived intelligence. We also interviewed the participants on their opinion about the interaction. Additionally, we video-coded the social signals participants showed during their interaction with the robot as well as the answers they provided the robot with. Our results show that participants liked the faulty robot significantly better than the robot that interacted flawlessly. We did not find significant differences in people's rating of the robot's anthropomorphism and perceived intelligence. The qualitative data confirmed the questionnaire results in showing that although the participants recognized the robot's mistakes, they did not necessarily reject the erroneous robot. The annotations of the video data further showed that gaze shifts (e.g., from and object to the robot or vice versa) and laughter are typical reactions to unexpected robot behavior. In contrast to existing research, we assess dimensions of user experience that have not been considered so far and we analyze the reactions users express when a robot makes a mistake. Our results show that decoding a human's social signals can help the robot understand that there is an error and subsequently react accordingly.
Highlights
Social robots are not yet in a technical state where they operate free from errors
We suppose that faulty instances of human–robot interaction (HRI) are full with knowledge that can help us further improve the interactional quality in new dimensions
In our effort to embrace the imperfections of social robots and create more believable robot characters, we propose to explore faulty robot behavior and the social signals humans show when a robot commits a mistake
Summary
Social robots are not yet in a technical state where they operate free from errors. Most research approaches act on the assumption of robots performing faultlessly. This results in a confined standpoint, in which the created scenarios are considered as gold standard. Alternatives resulting from unforeseeable conditions that develop during an experiment are often not further regarded or excluded. It lies within the nature of thorough scientific research to pursue a strict code of conduct. We think that because most research focuses on perfect interaction, many potentially crucial aspects are overlooked
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