Extending Quantitative Proxemics and Trust to HRI

Fanta Camara, Charles Fox

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Human-robot interaction (HRI) requires quantitative models of proxemics and trust for robots to use in negotiating with people for space. Hall's theory of proxemics has been used for decades to describe social interaction distances but has lacked detailed quantitative models and generative explanations to apply to these cases. In the limited case of autonomous vehicle interactions with pedestrians crossing a road, a recent model has explained the quantitative sizes of Hall's distances to 4% error and their links to the concept of trust in human interactions. The present study extends this model by generalising several of its assumptions to cover further cases including human-human and human-robot interactions. It tightens the explanations of Hall zones from 4% to 1% error and fits several more recent empirical HRI results. This may help to further unify these disparate fields and quantify them to a level which enables real-world operational HRI applications.

Original languageEnglish
Title of host publicationRO-MAN 2022 - 31st IEEE International Conference on Robot and Human Interactive Communication
Subtitle of host publicationSocial, Asocial, and Antisocial Robots
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages421-427
Number of pages7
ISBN (Electronic)9781728188591
DOIs
Publication statusPublished - 30 Sept 2022
Event31st IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2022 - Napoli, Italy
Duration: 29 Aug 20222 Sept 2022

Publication series

NameRO-MAN 2022 - 31st IEEE International Conference on Robot and Human Interactive Communication: Social, Asocial, and Antisocial Robots

Conference

Conference31st IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2022
Country/TerritoryItaly
CityNapoli
Period29/08/222/09/22

Bibliographical note

Funding Information:
This work has received funding from the EU H2020 project interACT: Designing cooperative interaction of automated vehicles with other road users in mixed traffic environments under grant agreement No 723395.

Publisher Copyright:
© 2022 IEEE.

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