  What we appreciate in dance is the ability of people to sponta- neously
improvise new movements and choreographies, sur- rendering to the music rhythm,
being inspired by the cur- rent perceptions and sensations and by previous
experiences, deeply stored in their memory. Like other human abilities, this,
of course, is challenging to reproduce in an artificial entity such as a robot.
Recent generations of anthropomor- phic robots, the so-called humanoids,
however, exhibit more and more sophisticated skills and raised the interest in
robotic communities to design and experiment systems devoted to automatic dance
generation. In this work, we highlight the importance to model a computational
creativity behavior in dancing robots to avoid a mere execution of
preprogrammed dances. In particular, we exploit a deep learning approach that
allows a robot to generate in real time new dancing move- ments according to to
the listened music.
