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  • 1.
    Kontogiorgos, Dimosthenis
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Abelho Pereira, André Tiago
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH, Tal-kommunikation.
    Sahindal, Boran
    KTH.
    van Waveren, Sanne
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Gustafson, Joakim
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Behavioural Responses to Robot Conversational Failures2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    Humans and robots will increasingly collaborate in domestic environments which will cause users to encounter more failures in interactions. Robots should be able to infer conversational failures by detecting human users’ behavioural and social signals. In this paper, we study and analyse these behavioural cues in response to robot conversational failures. Using a guided task corpus, where robot embodiment and time pressure are manipulated, we ask human annotators to estimate whether user affective states differ during various types of robot failures. We also train a random forest classifier to detect whether a robot failure has occurred and compare results to human annotator benchmarks. Our findings show that human-like robots augment users’ reactions to failures, as shown in users’ visual attention, in comparison to non-humanlike smart-speaker embodiments. The results further suggest that speech behaviours are utilised more in responses to failures when non-human-like designs are present. This is particularly important to robot failure detection mechanisms that may need to consider the robot’s physical design in its failure detection model.

  • 2.
    Kontogiorgos, Dimosthenis
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    van Waveren, Sanne
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Wallberg, Olle
    KTH.
    Abelho Pereira, André Tiago
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Leite, Iolanda
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Gustafson, Joakim
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Embodiment Effects in Interactions with Failing Robots2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    The increasing use of robots in real-world applications will inevitably cause users to encounter more failures in interactions. While there is a longstanding effort in bringing human-likeness to robots, how robot embodiment affects users’ perception of failures remains largely unexplored. In this paper, we extend prior work on robot failures by assessing the impact that embodiment and failure severity have on people’s behaviours and their perception of robots. Our findings show that when using a smart-speaker embodiment, failures negatively affect users’ intention to frequently interact with the device, however not when using a human-like robot embodiment. Additionally, users significantly rate the human-like robot higher in terms of perceived intelligence and social presence. Our results further suggest that in higher severity situations, human-likeness is distracting and detrimental to the interaction. Drawing on quantitative findings, we discuss benefits and drawbacks of embodiment in robot failures that occur in guided tasks.

  • 3.
    Li, Rui
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    van Almkerk, Marc
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    van Waveren, Sanne
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Carter, Elizabeth
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Leite, Iolanda
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Comparing Human-Robot Proxemics between Virtual Reality and the Real World2019Ingår i: HRI '19: 2019 14TH ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION, IEEE , 2019, s. 431-439Konferensbidrag (Refereegranskat)
    Abstract [en]

    Virtual Reality (VR) can greatly benefit Human-Robot Interaction (HRI) as a tool to effectively iterate across robot designs. However, possible system limitations of VR could influence the results such that they do not fully reflect real-life encounters with robots. In order to better deploy VR in HRI, we need to establish a basic understanding of what the differences are between HRI studies in the real world and in VR. This paper investigates the differences between the real life and VR with a focus on proxemic preferences, in combination with exploring the effects of visual familiarity and spatial sound within the VR experience. Results suggested that people prefer closer interaction distances with a real, physical robot than with a virtual robot in VR. Additionally, the virtual robot was perceived as more discomforting than the real robot, which could result in the differences in proxemics. Overall, these results indicate that the perception of the robot has to be evaluated before the interaction can be studied. However, the results also suggested that VR settings with different visual familiarities are consistent with each other in how they affect HRI proxemics and virtual robot perceptions, indicating the freedom to study HRI in various scenarios in VR. The effect of spatial sound in VR drew a more complex picture and thus calls for more in-depth research to understand its influence on HRI in VR.

  • 4.
    van Waveren, Sanne
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Björklund, Linnéa
    KTH.
    Carter, Elizabeth
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Leite, Iolanda
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, RPL.
    Knock on Wood: The Effects of Material Choice on the Perception of Social Robots2019Ingår i: Lecture Notes in Artificial Intelligence series (LNAI), 2019Konferensbidrag (Refereegranskat)
  • 5.
    van Waveren, Sanne
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Carter, Elizabeth J.
    KTH.
    Leite, Iolanda
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Robotik, perception och lärande, RPL.
    Take one for the team: The effects of error severity in collaborative tasks with social robots2019Ingår i: IVA 2019 - Proceedings of the 19th ACM International Conference on Intelligent Virtual Agents, Association for Computing Machinery (ACM), 2019, s. 151-158Konferensbidrag (Refereegranskat)
    Abstract [en]

    We explore the effects of robot failure severity (no failure vs. lowimpact vs. high-impact) on people's subjective ratings of the robot. We designed an escape room scenario in which one participant teams up with a remotely-controlled Pepper robot.We manipulated the robot's performance at the end of the game: The robot would either correctly follow the participant's instructions (control condition), the robot would fail but people could still complete the task of escaping the room (low-impact condition), or the robot's failure would cause the game to be lost (high-impact condition). Results showed no difference across conditions for people's ratings of the robot in terms of warmth, competence, and discomfort. However, people in the low-impact condition had significantly less faith in the robot's robustness in future escape room scenarios. Open-ended questions revealed interesting trends that are worth pursuing in the future: people may view task performance as a team effort and may blame their team or themselves more for the robot failure in case of a high-impact failure as compared to the low-impact failure.

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