Open AccessSegmentation and generalisation for writing skills transfer from humans to robots
Author(s) -
Li Chunxu,
Yang Chenguang,
Giannetti Cinzia
Publication year - 2019
Publication title -
cognitive computation and systems
Language(s) - English
Resource type - Journals
ISSN - 2517-7567
DOI - 10.1049/ccs.2018.0005
Subject(s) - robot , task (project management) , matlab , trajectory , computer science , artificial intelligence , segmentation , gaussian , position (finance) , computer vision , mixture model , operator (biology) , movement (music) , space (punctuation) , transfer (computing) , simulation , engineering , programming language , parallel computing , philosophy , systems engineering , repressor , chemistry , operating system , biochemistry , quantum mechanics , transcription factor , physics , finance , astronomy , economics , gene , aesthetics
In this study, the authors present an enhanced generalised teaching by demonstration technique for a KUKA iiwa robot. Movements are recorded from a human operator, and then the recorded data are sent to be segmented via MATLAB by using the difference method (DV). The outputted trajectories data are used to model a non‐linear system named dynamic movement primitive (DMP). For the purpose of learning from multiple demonstrations correctly and accurately, the Gaussian mixture model is employed for the evaluation of the DMP in order to modelling multiple trajectories by the teaching of demonstrator. Furthermore, a synthesised trajectory with smaller position errors in 3D space has been successfully generated by the usage of the Gaussian mixture regression algorithm. The proposed approach has been tested and demonstrated by performing a Chinese characters writing task with a KUKA iiwa robot.