  Robotic painting has been a subject of interest among both artists and
roboticists since the 1970s. Researchers and interdisciplinary artists have
employed various painting techniques and human-robot collaboration models to
create visual mediums on canvas. One of the challenges of robotic painting is
to apply a desired artistic style to the painting. Style transfer techniques
with machine learning models have helped us address this challenge with the
visual style of a specific painting. However, other manual elements of style,
i.e., painting techniques and brushstrokes of an artist, have not been fully
addressed. We propose a method to integrate an artistic style to the
brushstrokes and the painting process through collaboration with a human
artist. In this paper, we describe our approach to 1) collect brushstrokes and
hand-brush motion samples from an artist, and 2) train a generative model to
generate brushstrokes that pertains to the artist's style, and 3) fine tune a
stroke-based rendering model to work with our robotic painting setup. We will
report on the integration of these three steps in a separate publication. In a
preliminary study, 71% of human evaluators find our reconstructed brushstrokes
are pertaining to the characteristics of the artist's style. Moreover, 58% of
participants could not distinguish a painting made by our method from a
visually similar painting created by a human artist.
