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 [BibTeX] [Marc21]
Generating Calligraphic Trajectories with Model Predictive Control
Type of publication: Conference paper
Citation: Berio_GI_2017
Publication status: Published
Booktitle: Proc. 43rd Conf. on Graphics Interface
Year: 2017
Month: May
Pages: 132-139
Location: Edmonton, AL, Canada
DOI: 10.20380/GI2017.17
Abstract: We describe a methodology for the interactive definition of curves and motion paths using a stochastic formulation of optimal control. We demonstrate how the same optimization framework can be used in different ways to generate curves and traces that are geometrically and dynamically similar to the ones that can be seen in art forms such as calligraphy or graffiti art. The method provides a probabilistic description of trajectories that can be edited similarly to the control polygon typically used in the popular spline based methods. Furthermore, it also encapsulates movement kinematics, deformations and multivariate coordination. The user is then provided with a simple interactive interface that can generate multiple movements and traces at once, by visually defining a distribution of trajectories rather than a single one. The input to our method is a sparse sequence of targets defined as multivariate Gaussians. The output is a dynamical system generating curves that are natural looking and reflect the kinematics of a movement, similar to that produced by human drawing or writing.
Keywords: calligraphy, model predictive control, robot learning
Projects Idiap
Authors Berio, D.
Calinon, Sylvain
Leymarie, F. F.
Added by: [UNK]
Total mark: 0
  • Berio_GI_2017.pdf