Extending Quantitative Proxemics and Trust to HRI

TY - GEN

T1 - Extending Quantitative Proxemics and Trust to HRI

AU - Camara, Fanta

AU - Fox, Charles

N1 - 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.

PY - 2022/9/30

Y1 - 2022/9/30

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85140719730&partnerID=8YFLogxK

U2 - 10.1109/RO-MAN53752.2022.9900821

DO - 10.1109/RO-MAN53752.2022.9900821

M3 - Conference contribution

AN - SCOPUS:85140719730

T3 - RO-MAN 2022 - 31st IEEE International Conference on Robot and Human Interactive Communication: Social, Asocial, and Antisocial Robots

SP - 421

EP - 427

BT - RO-MAN 2022 - 31st IEEE International Conference on Robot and Human Interactive Communication

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2022

Y2 - 29 August 2022 through 2 September 2022

ER -