I Can See Your Aim: Estimating User Attention from Gaze for Handheld Robot Collaboration

Janis Stolzenwald, Walterio Mayol-Cuevas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
382 Downloads (Pure)

Abstract

This paper explores the estimation of user attention in the setting of a cooperative handheld robot — a robot designed to behave as a handheld tool but that has levels of task knowledge. We use a tool-mounted gaze tracking system, which, after modelling via a pilot study, we use as a proxy for estimating the attention of the user. This information is then used for cooperation with users in a task of selecting and engaging with objects on a dynamic screen. Via a video game setup, we test various degrees of robot autonomy from fully autonomous, where the robot knows what it has to do and acts, to no autonomy where the user is in full control of the task. Our results measure performance and subjective metrics and show how the attention model benefits the interaction and preference of users.
Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
Subtitle of host publicationProceedings of a meeting held 1-5 October 2018, Madrid, Spain
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3897-3904
Number of pages8
ISBN (Electronic)9781538680940
ISBN (Print)9781538680933
DOIs
Publication statusPublished - Mar 2019

Publication series

NameIEEE International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

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