  For long-term deployment in dynamic real-world environments, assistive robots
must continue to learn and adapt to their environments. Researchers have
developed various computational models for continual learning (CL) that can
allow robots to continually learn from limited training data, and avoid
forgetting previous knowledge. While these CL models can mitigate forgetting on
static, systematically collected datasets, it is unclear how human users might
perceive a robot that continually learns over multiple interactions with them.
In this paper, we developed a system that integrates CL models for object
recognition with a Fetch mobile manipulator robot and allows human participants
to directly teach and test the robot over multiple sessions. We conducted an
in-person study with 60 participants who interacted with our system in 300
sessions (5 sessions per participant). We conducted a between-participant study
with three different CL models (3 experimental conditions) to understand human
perceptions of continual learning robots over multiple sessions. Our results
suggest that participants' perceptions of trust, competence, and usability of a
continual learning robot significantly decrease over multiple sessions if the
robot forgets previously learned objects. However, the perceived task load on
participants for teaching and testing the robot remains the same over multiple
sessions even if the robot forgets previously learned objects. Our results also
indicate that state-of-the-art CL models might perform unreliably when applied
to robots interacting with human participants. Further, continual learning
robots are not perceived as very trustworthy or competent by human
participants, regardless of the underlying continual learning model or the
session number.
