Creative Robot Dance with Variational Encoder
This addresses the challenge of computational creativity in robotics for applications like entertainment or human-robot interaction, but appears incremental as it builds on existing deep learning methods for dance generation.
The paper tackled the problem of enabling robots to generate creative dance movements in real-time based on music, rather than executing preprogrammed sequences, using a deep learning approach.
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.