Impact of Robot Actions on Social Signals and Reaction Times in HRI Error Situations

Mirnig, Nicole; Giuliani, Manuel; Stollnberger, Gerald; Stadler, Susanne; Buchner, Roland; Tscheligi, Manfred

doi:10.1007/978-3-319-25554-5_46

Cited by 20 publications

(28 citation statements)

References 13 publications

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“…However, most users nevertheless followed the robot's order and threw the LEGO blocks to the floor. In addition to the previous results as reported in the study of Mirnig et al (2015), this result provides further evidence that users show specific social signals in response to robot errors. Future research should be targeted at making a robot understand the signals and make sense of them.…”

Section: Discussionsupporting

confidence: 82%

“…The second session was a LEGO task, where the robot invited the participant to build a few simple objects. We chose this setup in order to reenact the verbal context of the related work Mirnig et al, 2015). In addition, the interview session enabled us to collect qualitative data on the participants' opinions, which we included in our data analysis.…”

Section: Methodsmentioning

confidence: 99%

“…To base the user study on the previous findings from Giuliani et al (2015) and Mirnig et al (2015), we programmed the robot to commit two social norm violations and two technical failures in each session. Based on our previous research, we defined these two types of error as the typical mistakes robots make in HRI.…”

Section: Methodsmentioning

confidence: 99%

“…The induced errors were mainly modeled based on our previous findings on typical robot errors as reported in the studies of Giuliani et al (2015) and Mirnig et al (2015). Only LEGO task number 7 in the error condition was inspired by unusual requests as reported in the study of Salem et al (2015).…”

Section: User Study Designmentioning

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: Discussionsupporting

confidence: 82%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: User Study Designmentioning

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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“…Almost all errors researched in the literature exemplified technical failures (e.g., Gieselmann, 2006 ; Kim and Hinds, 2006 ; Gieselmann and Ostendorf, 2007 ; Spexard et al, 2008 ; Kim et al, 2009 ; Groom et al, 2010 ; Lee et al, 2010 ; Takayama et al, 2011 ; Desai et al, 2012 , 2013 ; Kahn et al, 2012 ; Rosenthal et al, 2012 ; Shiomi et al, 2013 ; Yasuda and Matsumoto, 2013 ; Kaniarasu and Steinfeld, 2014 ; Lohan et al, 2014 ; Cha et al, 2015 ; Gehle et al, 2015 ; Giuliani et al, 2015 ; Gompei and Umemuro, 2015 ; Hamacher, 2015 ; Knepper et al, 2015 ; Mirnig et al, 2015 , 2017 ; Mubin and Bartneck, 2015 ; Salem et al, 2015 ; Bajones et al, 2016 ; Brooks et al, 2016 ; Hamacher et al, 2016 ; Hayes et al, 2016 ; Ragni et al, 2016 ; Robinette et al, 2016 ; Engelhardt and Hansson, 2017 ; Law et al, 2017 ; Sarkar et al, 2017 ; van der Woerdt and Haselager, 2017 ; Kwon et al, 2018 ). Only a few evaluated the impact of social norm violations (e.g., Short et al, 2010 ; Salem et al, 2013 ; Giuliani et al, 2015 ; Mirnig et al, 2015 , 2017 ; van der Woerdt and Haselager, 2017 ), and none focused on human errors. Some articles did not specify the type of error used (e.g., Ross et al, 2004 ; Cassenti, 2007 ).…”

Section: Literature Review On User-centered Failure Handlingmentioning

confidence: 99%

Understanding and Resolving Failures in Human-Robot Interaction: Literature Review and Model Development

2018

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While substantial effort has been invested in making robots more reliable, experience demonstrates that robots operating in unstructured environments are often challenged by frequent failures. Despite this, robots have not yet reached a level of design that allows effective management of faulty or unexpected behavior by untrained users. To understand why this may be the case, an in-depth literature review was done to explore when people perceive and resolve robot failures, how robots communicate failure, how failures influence people's perceptions and feelings toward robots, and how these effects can be mitigated. Fifty-two studies were identified relating to communicating failures and their causes, the influence of failures on human-robot interaction (HRI), and mitigating failures. Since little research has been done on these topics within the HRI community, insights from the fields of human computer interaction (HCI), human factors engineering, cognitive engineering and experimental psychology are presented and discussed. Based on the literature, we developed a model of information processing for robotic failures (Robot Failure Human Information Processing, RF-HIP), that guides the discussion of our findings. The model describes the way people perceive, process, and act on failures in human robot interaction. The model includes three main parts: (1) communicating failures, (2) perception and comprehension of failures, and (3) solving failures. Each part contains several stages, all influenced by contextual considerations and mitigation strategies. Several gaps in the literature have become evident as a result of this evaluation. More focus has been given to technical failures than interaction failures. Few studies focused on human errors, on communicating failures, or the cognitive, psychological, and social determinants that impact the design of mitigation strategies. By providing the stages of human information processing, RF-HIP can be used as a tool to promote the development of user-centered failure-handling strategies for HRIs.

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