A Drink-Serving Mobile Social Robot Selects who to Interact with Using Gaze

Oskar Palinko, Kerstin Fischer, Eduardo Ruiz Ramírez, Lotte Damsgaard Nissen, Rosalyn M. Langedijk

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

Abstract

Robots will soon deliver food and beverages in various environments. These robots will need to communicate their intention efficiently; for example, they should indicate who they are addressing. We conducted a real-world study of a water serving robot at a university cafeteria. The robot was operated in a Wizard-of-Oz manner. It approached and offered water to students having their lunch. Our analyses of the relationship between robot gaze direction and the likelihood that someone takes a drink show that if people do not already have a drink and the interaction is not dominated by an overly enthusiastic user, the robot’s gaze behavior is effective in selecting an interaction partner even “in the wild”.
Original languageEnglish
Title of host publicationProceedings of the Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction
PublisherAssociation for Computing Machinery
Publication date23. Mar 2020
Pages384-385
ISBN (Electronic)9781450370578
DOIs
Publication statusPublished - 23. Mar 2020
Event15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020 - Cambridge, United Kingdom
Duration: 23. Mar 202026. Mar 2020

Conference

Conference15th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2020
CountryUnited Kingdom
CityCambridge
Period23/03/202026/03/2020
SponsorARM, Cambridge Consultants, et al., FN Robotics, Furhat Robotics, Halodi

Keywords

  • Gaze tracking
  • Human robot interaction
  • In the wild
  • Social robotics

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    Palinko, O., Fischer, K., Ramírez, E. R., Damsgaard Nissen, L., & Langedijk, R. M. (2020). A Drink-Serving Mobile Social Robot Selects who to Interact with Using Gaze. In Proceedings of the Companion of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 384-385). Association for Computing Machinery. https://doi.org/10.1145/3371382.3378339