How Do Human Users Teach a Continual Learning Robot in Repeated
  Interactions?
Ali Ayub Jainish Mehta Zachary De Francesco Patrick Holthaus Kerstin Dautenhahn Chrystopher L. Nehaniv
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/cl_hri.