Abstract
E-David (Electronic Drawing Apparatus for Vivid Image Display) is a system for controlling a variety of painting machines in order to create robotic paintings. This article summarizes the hardware set-up used for painting, along with recent developments, lessons learned from past painting machines, as well as plans for new approaches. We want to apply e-David as a platform for research towards improving automatic painting and to explore machine creativity. We present different painting machines, from small low-cost plotters to large industrial robots, and discuss the benefits and limitations of each type of platform and present their applicability to different tasks within the domain of robotic painting and artificial creativity research. A unified control interface with a scripting language allows users a simplified usage of different e-David-like machines. Furthermore, we present our system for automated stroke experimentation and recording, which is an advance towards allowing the machine to autonomously learn about brush dynamics. Finally, we also show how e-David can be used by artists “in the field” for different exhibitions.
Highlights
The e-David (Electronic Drawing Apparatus for Vivid Image Display) project was initiated at the University of Konstanz in 2009
We present a summary of the work done on upgrading the machine, an explanation of lessons learned about robotic painting in the last ten years, and how groundwork is being laid for future work
Machine Learning Groundwork: Through the enhanced data collection methods we plan to build a corpus of brushstrokes, which consist of pairs of trajectories and resulting strokes
Summary
The e-David (Electronic Drawing Apparatus for Vivid Image Display) project was initiated at the University of Konstanz in 2009. Hardware Flexibility: As industrial robots are expensive, difficult to set up, and require experience to operate, e-David should be usable on much simpler machines For this purpose, hardware should be mostly abstracted and it should be possible to integrate new machines that are potentially built in the future. The new software should allow both simple input of painting commands and a usable output of feedback images This will make e-David accessible for collaborating researchers, as development done on one painting process will carry over to all other machines in use. Machine Learning Groundwork: Through the enhanced data collection methods we plan to build a corpus of brushstrokes, which consist of pairs of trajectories and resulting strokes Using this dataset we intend to improve upon previous efforts at static stroke analysis [2] and use state-of-the-art machine learning approaches to allow the system to continuously improve its painting abilities on a technique level. Improved Autonomy: as an extension beyond current machine learning, the robot should be able to investigate unknown aspects of painting autonomously; this, e.g., includes searching the corpus of known strokes for gaps and running autonomous experiments to close these gaps
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