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Improving Sampling‐based Motion Control
Author(s) -
Liu Libin,
Yin KangKang,
Guo Baining
Publication year - 2015
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12571
Subject(s) - computer science , stylized fact , sampling (signal processing) , motion (physics) , key (lock) , implementation , artificial intelligence , noise (video) , motion control , computer vision , sample (material) , algorithm , chemistry , computer security , filter (signal processing) , chromatography , robot , economics , image (mathematics) , macroeconomics , programming language
We address several limitations of the sampling‐based motion control method of Liu et at. [LYvdP* 10]. The key insight is to learn from the past control reconstruction trials through sample distribution adaptation. Coupled with a sliding window scheme for better performance and an averaging method for noise reduction, the improved algorithm can efficiently construct open‐loop controls for long and challenging reference motions in good quality. Our ideas are intuitive and the implementations are simple. We compare the improved algorithm with the original algorithm both qualitatively and quantitatively, and demonstrate the effectiveness of the improved algorithm with a variety of motions ranging from stylized walking and dancing to gymnastic and Martial Arts routines.