  A service robot can provide a smoother interaction experience if it has the
ability to proactively detect whether a nearby user intends to interact, in
order to adapt its behavior e.g. by explicitly showing that it is available to
provide a service. In this work, we propose a learning-based approach to
predict the probability that a human user will interact with a robot before the
interaction actually begins; the approach is self-supervised because after each
encounter with a human, the robot can automatically label it depending on
whether it resulted in an interaction or not. We explore different
classification approaches, using different sets of features considering the
pose and the motion of the user. We validate and deploy the approach in three
scenarios. The first collects $3442$ natural sequences (both interacting and
non-interacting) representing employees in an office break area: a real-world,
challenging setting, where we consider a coffee machine in place of a service
robot. The other two scenarios represent researchers interacting with service
robots ($200$ and $72$ sequences, respectively). Results show that, even in
challenging real-world settings, our approach can learn without external
supervision, and can achieve accurate classification (i.e. AUROC greater than
$0.9$) of the user's intention to interact with an advance of more than $3$s
before the interaction actually occurs.
