What we appreciate in dance is the ability of people to spontaneously improvise new movements and choreographies, surrendering to the music rhythm, being inspired by the current 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 anthropomorphic 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 movements according to to the listened music.