2,513 research outputs found

    The perception of emotion in artificial agents

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    Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

    A systematic comparison of affective robot expression modalities

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    Theory of Robot Communication: II. Befriending a Robot over Time

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    In building on theories of Computer-Mediated Communication (CMC), Human-Robot Interaction, and Media Psychology (i.e. Theory of Affective Bonding), the current paper proposes an explanation of how over time, people experience the mediated or simulated aspects of the interaction with a social robot. In two simultaneously running loops, a more reflective process is balanced with a more affective process. If human interference is detected behind the machine, Robot-Mediated Communication commences, which basically follows CMC assumptions; if human interference remains undetected, Human-Robot Communication comes into play, holding the robot for an autonomous social actor. The more emotionally aroused a robot user is, the more likely they develop an affective relationship with what actually is a machine. The main contribution of this paper is an integration of Computer-Mediated Communication, Human-Robot Communication, and Media Psychology, outlining a full-blown theory of robot communication connected to friendship formation, accounting for communicative features, modes of processing, as well as psychophysiology.Comment: Hoorn, J. F. (2018). Theory of robot communication: II. Befriending a robot over time. arXiv:cs, 2502572(v1), 1-2

    First impressions: A survey on vision-based apparent personality trait analysis

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

    Affective Computing for Human-Robot Interaction Research: Four Critical Lessons for the Hitchhiker

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    Social Robotics and Human-Robot Interaction (HRI) research relies on different Affective Computing (AC) solutions for sensing, perceiving and understanding human affective behaviour during interactions. This may include utilising off-the-shelf affect perception models that are pre-trained on popular affect recognition benchmarks and directly applied to situated interactions. However, the conditions in situated human-robot interactions differ significantly from the training data and settings of these models. Thus, there is a need to deepen our understanding of how AC solutions can be best leveraged, customised and applied for situated HRI. This paper, while critiquing the existing practices, presents four critical lessons to be noted by the hitchhiker when applying AC for HRI research. These lessons conclude that: (i) The six basic emotions categories are irrelevant in situated interactions, (ii) Affect recognition accuracy (%) improvements are unimportant, (iii) Affect recognition does not generalise across contexts, and (iv) Affect recognition alone is insufficient for adaptation and personalisation. By describing the background and the context for each lesson, and demonstrating how these lessons have been learnt, this paper aims to enable the hitchhiker to successfully and insightfully leverage AC solutions for advancing HRI research.Comment: 11 pages, 3 figures, 1 tabl
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