Reconfiguring spatial formation arrangement by robot body orientation

Kuzuoka,; Сузуки,; Yamashita,; Yamazaki,

doi:10.1109/hri.2010.5453182

Cited by 55 publications

(43 citation statements)

References 14 publications

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“…In the case of two participants, typical F-formation arrangements are vis-a-vis, L-shape, and side-by-side. When there are more than three participants, a circular formation is typically formed [18].…”

Section: Introductionmentioning

confidence: 99%

“…In our case, we analyze quasi-stationary people in an unconstrained scenario identifying those subjects engaged in a face-to-face interaction, i.e., a scene monitored by a single camera where a variable amount of people (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) is present. We import into the analysis the sociological concept of F-formation as defined by Adam Kendon in the late '70s [14,15,16,17], commonly adopted in the sociological literature.…”

Section: Introductionmentioning

confidence: 99%

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Social interaction discovery by statistical analysis of F-formations

Cristani¹,

Bazzani²,

Paggetti³

et al. 2011

Procedings of the British Machine Vision Conference 2011

179

265

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We present a novel approach for detecting social interactions in a crowded scene by employing solely visual cues. The detection of social interactions in unconstrained scenarios is a valuable and important task, especially for surveillance purposes. Our proposal is inspired by the social signaling literature, and in particular it considers the sociological notion of F-formation. An F-formation is a set of possible configurations in space that people may assume while participating in a social interaction. Our system takes as input the positions of the people in a scene and their (head) orientations; then, employing a voting strategy based on the Hough transform, it recognizes F-formations and the individuals associated with them. Experiments on simulations and real data promote our idea.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social interaction discovery by statistical analysis of F-formations

Cristani¹,

Bazzani²,

Paggetti³

et al. 2011

Procedings of the British Machine Vision Conference 2011

179

265

View full text Add to dashboard Cite

show abstract

“…Complex as they are, these dynamics allow for many interesting applications. For example, by relying on people to get out of the way for a navigating robot [17], to signal approachability with a group of virtual agents [18], or to influence the formation of people interacting with a robot [19]. As evidenced by these papers, the temporal and social dynamics are relevant and can have a strong influence on what happens in the interaction.…”

Section: B Temporal and Social Dynamics Of Interactionmentioning

confidence: 99%

Dynamics of social positioning patterns in group-robot interactions

Vroon

Joosse

Lohse

et al. 2015

2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

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Abstract-When a mobile robot interacts with a group of people, it has to consider its position and orientation. We introduce a novel study aimed at generating hypotheses on suitable behavior for such social positioning, explicitly focusing on interaction with small groups of users and allowing for the temporal and social dynamics inherent in most interactions. In particular, the interactions we look at are approach, converse and retreat. In this study, groups of three participants and a telepresence robot (controlled remotely by a fourth participant) solved a task together while we collected quantitative and qualitative data, including tracking of positioning/orientation and ratings of the behaviors used. In the data we observed a variety of patterns that can be extrapolated to hypotheses using inductive reasoning. One such pattern/hypothesis is that a (telepresence) robot could pass through a group when retreating, without this affecting how comfortable that retreat is for the group members. Another is that a group will rate the position/orientation of a (telepresence) robot as more comfortable when it is aimed more at the center of that group.

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“…The position based upon the developed model was preferred over positions in which the robot was placed either close to the object or to the listener. Kuzuoka et al [11] investigated the capability of an information-providing robot to change the F-formation of a group of listeners. The underlying premise is that a robot which can change the Fformation can thereby direct the attention of its listeners.…”

Section: F-formationsmentioning

confidence: 99%

Cultural differences in how an engagement-seeking robot should approach a group of people

Joosse

Poppe

Lohse

et al. 2014

Proceedings of the 5th ACM International Conference on Collaboration Across Boundaries: Culture, Distance &Amp; Technology

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In our daily life everything and everyone occupies an amount of space, simply by "being there". Edward Hall coined the term proxemics for the studies of man's use of this space. This paper presents a study on proxemics in Human-Robot Interaction and particularly on robot's approaching groups of people. As social psychology research found proxemics to be culturally dependent, we focus on the question of the appropriateness of the robot's approach behavior in different cultures. We present an online survey (N=181) that was distributed in three countries; China, the U.S. and Argentina. Our results show that participants prefer a robot that stays out of people's intimate space zone just like a human would be expected to do. With respect to cultural differences, Chinese participants showed high-contact responses and believed closer approaches were appropriate compared to their U.S. counterparts. Argentinian participants more closely resembled the ratings of the U.S. participants.

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Reconfiguring spatial formation arrangement by robot body orientation

Cited by 55 publications

References 14 publications

Social interaction discovery by statistical analysis of F-formations

Social interaction discovery by statistical analysis of F-formations

Dynamics of social positioning patterns in group-robot interactions

Cultural differences in how an engagement-seeking robot should approach a group of people

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