Social robots coexist with humans in situations where they have to
exhibit proper communication skills. Since users may have different
features and communicative procedures, personalizing human–robot
interactions is essential for the success of these interactions. This
manuscript presents Active Learning based on computer vision and
human–robot interaction for user recognition and profiling to
personalize robot behavior. The system identifies people using
Intel-face-detection-retail-004 and FaceNet for face recognition and
obtains users’ information through interaction. The system aims to
improve human–robot interaction by (i) using online learning to allow
the robot to identify the users and (ii) retrieving users’ information
to fill out their profiles and adapt the robot’s behavior. Since user
information is necessary for adapting the robot for each interaction, we
hypothesized that users would consider creating their profile by
interacting with the robot more entertaining and easier than taking a
survey. We validated our hypothesis with three scenarios: the
participants completed their profiles using an online survey, by
interacting with a dull robot, or with a cheerful robot. The results
show that participants gave the cheerful robot a higher usability score (82.14/100 points), and they were more entertained while creating their profiles
with the cheerful robot than in the other scenarios. Statistically
significant differences in the usability were found between the
scenarios using the robot and the scenario that involved the online
survey. Finally, we show two scenarios in which the robot interacts with
a known user and an unknown user to demonstrate how it adapts to the
situation.
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