Time Generalization of Trajectories Learned on Articulated Soft Robots
Franco Angelini, Riccardo Mengacci, Cosimo Della Santina, Manuel G. Catalano, Manolo Garabini, Antonio Bicchi, Giorgio Grioli
Abstract
This letter proposes a framework which is able to generate a sequence of three-dimensional human dance poses for a given music. The proposed framework consists of three components: a music feature encoder, a pose generator, and a music genre classifier. We focus on integrating these components for generating a realistic 3D human dancing move from music, which can be applied to artificial agents and humanoid robots. The trained dance pose generator, which is a generative autoregressive model, is able to synthesize a dance sequence longer than 1,000 pose frames. Experimental results of generated dance sequences from various songs show how the proposed method generates human-like dancing move to a given music. In addition, a generated 3D dance sequence is applied to a humanoid robot, showing that the proposed framework can make a robot to dance just by listening to music.
BibTeX
@inproceedings{ral2020_timegeneralizati,
title = {Time Generalization of Trajectories Learned on Articulated Soft Robots},
author = {Franco Angelini and Riccardo Mengacci and Cosimo Della Santina and Manuel G. Catalano and Manolo Garabini and Antonio Bicchi and Giorgio Grioli},
booktitle = {RA-L 2020},
year = {2020}
}