Enabling robots to adhere to social norms by detecting F-formations

Avgi Kollakidou*, Lakshadeep Naik, Oskar Palinko, Leon Bodenhagen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

5 Downloads (Pure)

Abstract

Robot navigation in environments shared with humans should take into account social structures and interactions. The identification of social groups has been a challenge for robotics as it encompasses a number of disciplines. We propose a hierarchical clustering method for grouping individuals into free standing conversational groups (FSCS), utilising their position and orientation. The proposed method is evaluated on the SALSA dataset with achieved F1 score of 0.94. The algorithm is also evaluated for scalability and implemented on a mobile robot attempting to detect social groups and engage in interaction.

Original languageEnglish
Title of host publication2021 30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
PublisherIEEE
Publication date8. Aug 2021
Pages110-116
ISBN (Electronic)9781665404921
DOIs
Publication statusPublished - 8. Aug 2021
Event30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021 - Virtual, Vancouver, Canada
Duration: 8. Aug 202112. Aug 2021

Conference

Conference30th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2021
Country/TerritoryCanada
CityVirtual, Vancouver
Period08/08/202112/08/2021
SeriesIEEE RO-MAN proceedings
ISSN1944-9445

Fingerprint

Dive into the research topics of 'Enabling robots to adhere to social norms by detecting F-formations'. Together they form a unique fingerprint.

Cite this