How do Human Users Teach a Continual Learning Robot in Repeated Interactions?

Ali Ayub, Zachary Francesco, Patrick Holthaus, Kerstin Dautenhahn, Chrystopher Nehaniv

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github. com/aliayub7/c1-hri.
Original languageEnglish
Title of host publication2023 32nd IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)
Place of PublicationBusan, Korea, Republic of
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1975-1982
Number of pages8
Edition32
ISBN (Electronic)979-8-3503-3670-2
DOIs
Publication statusPublished - 13 Nov 2023
Event32nd IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2023) - Busan, Korea, Democratic People's Republic of
Duration: 28 Aug 202331 Aug 2023
https://www.ro-man2023.org/main

Conference

Conference32nd IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2023)
Abbreviated titleIEEE RO-MAN 2023
Country/TerritoryKorea, Democratic People's Republic of
CityBusan
Period28/08/2331/08/23
Internet address

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