Learning bowing gesture with motion diversity by dynamic movement primitives

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Motion diversity is an important factor in resembling motion with human-likeness. In this paper, we propose a method by which a robot acquires gesturing skills with motion diversity. We measured the bowing gesture with a 3D capturing camera and used dynamic movement primitives (DMPs) in order to capture the distribution of kernel parameters. The results showed that the height of kernel parameters have a Gaussian distribution and that the trajectory regenerated with the calculated parameters had the desired motion diversity.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Issue Date
2017-07-25
Language
English
Citation

14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017, pp.165 - 166

DOI
10.1109/URAI.2017.7992701
URI
http://hdl.handle.net/10203/237977
Appears in Collection
ME-Conference Papers(학술회의논문)
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