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) integrate the learned model on a robot arm to paint on a canvas . In a preliminary study, 71\% of human evaluators find our robot's paintings pertaining to the characteristics of the artist's style  .
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 .
A
,
R
integrate the learned model on a robot arm to paint on a canvas
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
R
robot's paintings
reconstructed brushstrokes are
A
. Moreover, 58\% of participants could not distinguish a painting made by our method from a visually similar painting created by a human artist