The paper describes a novel approach to allow a robot to dance following musical rhythm. The proposed system generates a dance for a humanoid robot through the combination of basic movements synchronized with the music. The system made up of three parts: the extraction of features from audio file, estimation of movements through the Hidden Markov Models and, finally, the generation of dance. Starting from a set of given movements, the robot choices sequence of movements a suitable Hidden Markov Model, and synchronize them processing musical input. The proposed approach has the advantage that movement execution probabilities could be changed according evaluation of the dance execution in order to have an artificial creative system. In the same way, a choreographer could give more importance to some movements and/or exclude others, using the system as a co-creation tool. The approach has been tested on Aldebaran NAO humanoid using different genres of music, and experimentations was conduct at presence of real human dancers to have feedback of the goodness of the robot execution. Three professional judges expressed their evaluations about the following points: appropriateness of movements for a given musical genre; the precision to track the rhythm; the aesthetic impact of the whole sequence of movements; and the overall judgment of the robot performance. All the evaluations are very satisfying, and confirm that robot dance is realistic and aesthetically acceptable. The robustness and flexibility of the system allow us to embed the system in artificial creative system in future work. In the discussion we introduce some issues to pursuit this aim, using a previous proposed cognitive architecture based on needs and motivations.
|Number of pages||9|
|Journal||Biologically Inspired Cognitive Architectures|
|Publication status||Published - 2016|
All Science Journal Classification (ASJC) codes
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Artificial Intelligence