Errare humanum est: Erroneous robots in human-robot interaction

Ragni, Marco; Rudenko, Andrey; Kühnert, Barbara; Arras, Kai O.

doi:10.1109/roman.2016.7745164

Cited by 44 publications

(37 citation statements)

References 17 publications

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“…Therefore, incongruent multimodal robot behavior results in a social norm violation. Ragni et al (2016) reported similar effects. The researchers performed a study in which a human and a robot competed against each other in a reasoning task and a memory task.…”

Section: Introductionmentioning

confidence: 61%

“…To overcome this error situation, participants changed their social signals, but on the other hand they rated the likability of the robot higher. Similarly, Ragni et al (2016) showed that the participants in their study enjoyed the interaction with the faulty robot significantly more, than the participants who had interacted with a flawless robot. On the other hand, their participants who had interacted with the faulty robot, rated it less intelligent, less competent, and less superior, which again confirms the Pratfall Effect.…”

Section: Discussionmentioning

confidence: 84%

“…That is, how extensive can an error become until it becomes a deal-breaker. Ragni et al (2016) already provided evidence that erroneous robot behavior decreases performance of a human interacting with the robot. It could also be interesting to explore how users react in case of the robot giving ambiguous information.…”

Section: Discussionmentioning

confidence: 99%

“…Robots that commit errors, on the other hand, could then be viewed as more human-like and, in subsequence, more likeable. With their study on an erroneous robot in a competitive game-play scenario Ragni et al (2016) provided additional evidence that people consider robots in general as competent, functional, and intelligent.…”

Section: Introductionmentioning

confidence: 99%

“…While the errors in the study of Ragni et al (2016) were based on errors from HHI, the errors we used were modeled based on data from HRI. Our work and that of Ragni et al (2016) further cover different aspects: (a) their errors were task-related, ours non-task-related; (b) they covered the cognitive ability of the robot and we dealt with socially (in) appropriate robot behavior and more general soft-and hardware problems; and (c) they assessed the overall enjoyment of the interaction and users' task performance, while we looked into the interconnectedness of likability, anthropomorphism, and intelligence. We chose to examine these factors since they are commonly used and accepted measures in the HRI domain.…”

Section: Introductionmentioning

confidence: 99%

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To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot

et al. 2017

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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 the 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 ratings 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 an 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.

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Section: Introductionmentioning

confidence: 61%

Section: Discussionmentioning

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot

et al. 2017

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The effect of anthropomorphized technology failure on the desire to connect with others

Lteif

Valenzuela

2022

Psychology and Marketing

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Extant work suggests that unsuccessful human−technology interactions can elicit negative affective reactions, prompting users to engage in compensatory behavior including seeking affiliation with others. The current work presents one mechanism to explain these findings. Specifically, we propose that users may construe incidents of technology failure akin to incidents of social rejection: Across three studies, we demonstrate that when an anthropomorphized (vs. nonanthropomorphized) technology fails to function as expected, users experience feelings of rejection, and subsequently express a greater desire to connect with others. In doing so, we contribute to extant research on human−technology interactions by uniquely demonstrating that feelings of social rejection may arise from technology failure. Our work also deepens our understanding of the unintended negative consequences of product anthropomorphism and, as such, provides insight into technology design.

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