Footing in human-robot conversations: How robots might shape participant roles using gaze cues

By Bilge Mutlu, Toshiyuki Shiwa, Takayuki Kanda, Hiroshi Ishiguro, Norihiro Hagita. IEEE/ACM International Conference on Human-Robot Interaction, 2009

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During conversations, speakers establish their and others' participant roles (who participates in the conversation and in what capacity)-or “footing” as termed by Goffman-using gaze cues. In this paper, we study how a robot can establish the participant roles of its conversational partners using these cues. We designed a set of gaze behaviors for Robovie to signal three kinds of participant roles: addressee, bystander, and overhearer. We evaluated our design in a controlled laboratory experiment with 72 subjects in 36 trials. In three conditions, the robot signaled to two subjects, only by means of gaze, the roles of (1) two addressees, (2) an addressee and a bystander, or (3) an addressee and an overhearer. Behavioral measures showed that subjects' participation behavior conformed to the roles that the robot communicated to them. In subjective evaluations, significant differences were observed in feelings of groupness between addressees and others and liking between overhearers and others. Participation in the conversation did not affect task performance-measured by recall of information presented by the robot-but affected subjects' ratings of how much they attended to the task.

Subfields: Experimental and Cognitive Psychology, Language and Linguistics, Social Psychology

View Paper: https://www.semanticscholar.org/paper/cace5c2e949b49647202f6b381f43d0a9aca75be

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Citations in the corpus

Expressing thought: Improving robot readability with animation principles (2011, background): “, addressee, bystander, and overhearer; this influences how involved people feel in the conversation and how they feel toward the robot (a Robovie R2) [18].”
It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation (2016, background): “A large number of works in computer vision have studied the problem of estimating human eye gaze [7] given its importance for different applications, such as human-robot interaction [21], affective computing [4], and social signal processing [30].”
To Err is Human(-like): Effects of Robot Gesture on Perceived Anthropomorphism and Likability (2013, background): “Since a large body of research (e.g., [17, 23, 26, 27]) has already focused on the role of robot gaze in human-robot interaction (HRI), our investigations concentrate on hand and arm gesture as a speciﬁc subpart of non-verbal communicative behavior.”
Collaborative Effort towards Common Ground in Situated Human-Robot Dialogue (2014, background): “Non-verbal modalities such as gesture and gaze have been investigated extensively in HRI literature, for example, for engagement [1, 19, 23], and turn taking and participation [16].”
Recognizing engagement in human-robot interaction (2010, background): “[12] have studied the interaction of gaze and turn-taking [15] using a humanoid robot.”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “Regarding turn-taking in robots, a computational strategy for robots participating in group conversation is presented in [182], and the very important role of gaze cues in turn taking and participant role assignment in human-robot conversations is examined in [183].”
Social eye gaze in human-robot interaction (2017, background): “…a robot can use gaze behaviors to manipulate certain members of a group into taking conversational roles such as onlooker, active participant, or listener (Kirchner, Alempijevic, & Dissanayake, 2011; Mutlu, Kanda, Forlizzi, Hodgins, & Ishiguro, 2012; Mutlu, Shiwa, Kanda, Ishiguro, & Hagita, 2009).”