  Continuously measuring the engagement of users with a robot in a Human-Robot
Interaction (HRI) setting paves the way towards in-situ reinforcement learning,
improve metrics of interaction quality, and can guide interaction design and
behaviour optimisation. However, engagement is often considered very
multi-faceted and difficult to capture in a workable and generic computational
model that can serve as an overall measure of engagement. Building upon the
intuitive ways humans successfully can assess situation for a degree of
engagement when they see it, we propose a novel regression model (utilising CNN
and LSTM networks) enabling robots to compute a single scalar engagement during
interactions with humans from standard video streams, obtained from the point
of view of an interacting robot. The model is based on a long-term dataset from
an autonomous tour guide robot deployed in a public museum, with continuous
annotation of a numeric engagement assessment by three independent coders. We
show that this model not only can predict engagement very well in our own
application domain but show its successful transfer to an entirely different
dataset (with different tasks, environment, camera, robot and people). The
trained model and the software is available to the HRI community as a tool to
measure engagement in a variety of settings.
