Automatic aesthetics assessment of robotic dance motions

Abstract

Human dancers can understand and judge the aesthetics of their own dance motions from their movement perception. Inspired by this, we propose a novel mechanism of automatic aesthetics assessment of robotic dance motions, which is based on ensemble learning aimed at developing the autonomous judgment ability of robots. In the proposed mechanism, key pose descriptors based higher-order clustering features are designed to characterize robotic dance motion. Then, an ensemble classifier is built to train a machine aesthetics model for the automatic aesthetics assessment on robotic dance motions. The proposed mechanism has been implemented on a simulated robot environment, and experimental results show its feasibility and good performance.