To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot
To Err Is Robot: How Humans Assess and Act toward an Erroneous Social Robot
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DOI：
10.3389/frobt.2017.00021
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发表时间：
2017-05-31
影响因子：
3.4
通讯作者：
Tscheligi, Manfred
中科院分区：
文献类型：
--
作者：
Mirnig, Nicole;Stollnberger, Gerald;Tscheligi, Manfred
我们进行了一项用户研究，有目的地将错误行为编程到机器人的例程中。我们的目的是探索参与者对有缺陷的机器人与无错误的机器人的评价是否不同，以及人们在与有缺陷的机器人互动时表现出哪些反应。这项研究基于我们之前对机器人错误的研究，我们检测了典型的错误情况以及参与者在社交人机交互过程中产生的社交信号。与我们之前的工作相反，我们研究了机器人无意中发生错误的视频材料，在此报告的用户研究中，我们有目的地引发机器人错误，以进一步探索机器人错误后人类互动伙伴的社交信号。我们的参与者与类似人类的 NAO 进行交互，机器人要么执行错误，要么没有错误。首先，机器人询问参与者一系列预先定义的问题，然后要求他们完成几个乐高拼搭任务。互动结束后，我们要求参与者对机器人的拟人化、可爱度和感知智力进行评分。我们还采访了参与者，了解他们对互动的看法。此外，我们对参与者在与机器人互动过程中表现出的社交信号以及他们向机器人提供的答案进行了视频编码。我们的结果表明，参与者对有缺陷的机器人的喜爱程度明显高于对交互完美的机器人的喜爱程度。我们没有发现人们对机器人拟人化和感知智力的评价存在显着差异。定性数据证实了问卷结果，表明尽管参与者认识到机器人的错误，但他们并不一定拒绝错误的机器人。视频数据的注释进一步表明，目光转移（例如，从物体到机器人，反之亦然）和笑声是对意外机器人行为的典型反应。与现有研究相反，我们评估了迄今为止尚未考虑的用户体验维度，并分析了机器人犯错时用户表达的反应。我们的结果表明，解码人类的社交信号可以帮助机器人了解存在错误并随后做出相应反应。
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.