What Could Go Wrong: Lessons Learned When Doing HRI User Studies with Off-the-Shelf Social Robots

An Jacobs, Mike Ligthart, Shirley A. Elprama, Koen Hindriks, Katie Winkle

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Today, off-the-shelf social robots are used increasingly in the HRI community to research social interactions with different target user groups across a range of domains (e.g. healthcare, education, retail and other public spaces). We invite everyone doing HRI studies with end users, in the lab or in the wild, to collect past experiences of methods and practices that had issues or did not turn out as expected. This could include but is not limited to experimental setup, unplanned interactions, or simply the difficulty in transferring theory to the real world. In order to be able to generalize and compare differences across multiple HRI domains and create common solutions, we are focusing in this workshop on experiences with often used off-the-shelf social robots. We are interested in identifying the underlying causes of the unexpected HRI results, e.g. the contextual, task, and user related factors that influence interaction with a robot platform. We will furthermore discuss and document (ad hoc) solutions and/or lessons learned such that they can be shared with the HRI community. As well as sharing specific case studies documenting real world HRI experiences, we further hope to inspire the continued sharing of open and insightful reflections within the HRI community.

Original languageEnglish
Title of host publicationHRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction
PublisherACM, IEEE Computer Society
Pages395-396
Number of pages2
ISBN (Electronic)9781450356152
DOIs
Publication statusPublished - 1 Mar 2018
Externally publishedYes
Event13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018 - Chicago, United States
Duration: 5 Mar 20188 Mar 2018

Conference

Conference13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018
CountryUnited States
CityChicago
Period5/03/188/03/18

Fingerprint

Robots
Education

Keywords

  • lessons learned
  • social robots
  • user studies

Cite this

Jacobs, A., Ligthart, M., Elprama, S. A., Hindriks, K., & Winkle, K. (2018). What Could Go Wrong: Lessons Learned When Doing HRI User Studies with Off-the-Shelf Social Robots. In HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 395-396). ACM, IEEE Computer Society. https://doi.org/10.1145/3173386.3173564
Jacobs, An ; Ligthart, Mike ; Elprama, Shirley A. ; Hindriks, Koen ; Winkle, Katie. / What Could Go Wrong : Lessons Learned When Doing HRI User Studies with Off-the-Shelf Social Robots. HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, IEEE Computer Society, 2018. pp. 395-396
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Jacobs, A, Ligthart, M, Elprama, SA, Hindriks, K & Winkle, K 2018, What Could Go Wrong: Lessons Learned When Doing HRI User Studies with Off-the-Shelf Social Robots. in HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, IEEE Computer Society, pp. 395-396, 13th Annual ACM/IEEE International Conference on Human Robot Interaction, HRI 2018, Chicago, United States, 5/03/18. https://doi.org/10.1145/3173386.3173564

What Could Go Wrong : Lessons Learned When Doing HRI User Studies with Off-the-Shelf Social Robots. / Jacobs, An; Ligthart, Mike; Elprama, Shirley A.; Hindriks, Koen; Winkle, Katie.

HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, IEEE Computer Society, 2018. p. 395-396.

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Jacobs A, Ligthart M, Elprama SA, Hindriks K, Winkle K. What Could Go Wrong: Lessons Learned When Doing HRI User Studies with Off-the-Shelf Social Robots. In HRI 2018 - Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction. ACM, IEEE Computer Society. 2018. p. 395-396 https://doi.org/10.1145/3173386.3173564