文献类型：期刊文献

英文题名：Automatic aesthetics assessment of robotic dance motions

作者：Peng, Hua[1,2,3];Li, Jing[4];Hu, Huosheng[3];Hu, Keli[1];Zhao, Liping[1];Tang, Chao[5]

机构：[1]Shaoxing Univ, Dept Comp Sci & Engn, Shaoxing 312000, Peoples R China;[2]Jishou Univ, Coll Informat Sci & Engn, Jishou 416000, Peoples R China;[3]Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England;[4]Shaoxing Univ, Acad Arts, Shaoxing 312000, Peoples R China;[5]Hefei Univ, Sch Artificial Intelligence & Big Data, Hefei 230601, Peoples R China

年份：2022

卷号：155

外文期刊名：ROBOTICS AND AUTONOMOUS SYSTEMS

收录：SCI-EXPANDED(收录号：WOS:000833416900006)、、EI(收录号：20222412220244)、Scopus(收录号：2-s2.0-85131663230)、WOS

基金：This work was supported by National Natural Science Foundation of China (Grant No. 61662025 , 61871289 ), and Zhejiang Provincial Natural Science Foundation of China (Grant No. LY19F020015 , LY20F030006 , LY20F020011 ), Humanities and Social Sciences Research Foundation of the Ministry of Education of China (Grant No. 21YJAZH065 , 21YJCZH039 ), and Anhui Provincial Natural Science Foundation, China (Grant No. 2008085MF202 ), and University Natural Sciences Research Project of Anhui Province (Grant No. KJ2020A0660 ).

语种：英文

外文关键词：Machine aesthetics; Ensemble learning; Kinematic perception

外文摘要：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. (C) 2022 Elsevier B.V. All rights reserved.