イクノ　ソウイチロウ   Soichiro Ikuno   生野　壮一郎    所属   コンピュータサイエンス学部 コンピュータサイエンス学科    職種   教授
言語種別	英語
発行・発表の年月	2021/06
形態種別	学術論文
査読	査読あり
標題	A deep learning framework for realistic robot motion generation
執筆形態	共著
掲載誌名	Neural Computing and Applications
掲載区分	国外
出版社・発行元	Springer
著者・共著者	Ran Dong, Qiong Chang, Soichiro Ikuno
概要	Humanoid robots are being developed to play the role of personal assistants. With the development of artificial intelligence technology, humanoid robots are expected to perform many human tasks, such as housework, human care, and even medical treatment. However, robots cannot currently move flexibly like humans, which affects their fine motor skill performance. This is primarily because traditional robot control methods use manipulators that are difficult to articulate well. To solve this problem, we propose a nonlinear realistic robot motion generation method based on deep learning. Our method benefits from decomposing human motions into basic motions and realistic motions using the multivariate empirical mode decomposition and learning the biomechanical relationships between them by using an autoencoder generation network. The experimental results show that realistic motion features can be learned by the generation network and motion realism can be increased by adding the learned motions to the robots.