  In many joint-action scenarios, humans and robots have to coordinate their
movements to accomplish a given shared task. Lifting an object together, sawing
a wood log, transferring objects from a point to another are all examples where
motor coordination between humans and machines is a crucial requirement. While
the dyadic coordination between a human and a robot has been studied in
previous investigations, the multi-agent scenario in which a robot has to be
integrated into a human group still remains a less explored field of research.
In this paper we discuss how to synthesise an artificial agent able to
coordinate its motion in human ensembles. Driven by a control architecture
based on deep reinforcement learning, such an artificial agent will be able to
autonomously move itself in order to synchronise its motion with that of the
group while exhibiting human-like kinematic features. As a paradigmatic
coordination task we take a group version of the so-called mirror-game which is
highlighted as a good benchmark in the human movement literature.
