## 2 Related Work  
Apart from e-David, a multitude of other painting machines are currently used in the art world and in research. Historical examples have been discussed extensively in [2], thus in the following we only list contemporary examples. One of them is Busker, a UR-10-based painting robot under development by [PERSON] et al. [8] at the University of Udine. They use a collaborative robotic arm that is able to swap between brushes and watercolors. It recreates digital images by using Non-Photorealistic Rendering (NPR) techniques to convert them into strokes. Measurements about watercolor and brush properties are taken into consideration by the NPR methods, which include hatching and gradient-based random strokes [9; 10]. [PERSON] et al. [11] use Baxter, a commercially available compliant robot, to replicate a graffiti-style of writing. They use the Sigma-Lognormal model to represent handwriting in segments, which are collected into a motion plan of the entire graffiti. A specialized kinematic model is developed to allow for smooth, compliant robot movement in order to achieve human-like writing motions. Furthermore, they employ an iterative correction scheme, as the fairly relaxed motion control allows inaccuracies to accumulate which must be corrected. [PERSON] et al. [11] results are human-like graffiti, written in thick felt-pen. [PERSON] et al. [12] propose a method to stylize faces by comparing feature points of an input face to a reference face. They use the offsets of triangularizations of the faces as a difference metric and detect important features of the face from this. They also apply an exaggeration to the most salient features to achieve a caricature-like effect. For painting on their machine, they segment the face into six regions, which are painted in a predetermined order with a SCARA robot holding a pencil. Strokes are placed according to a binarization of the stylized image. However, the results shown by [PERSON] et al. [12] indicate that they do not decompose the image into strokes, but fill in dark regions with a pattern. [PERSON] et al. [13] use a KUKA iiwa 7 DoF robot to draw on an arbitrary surface using impedance control. An artist drawing on a tablet provides input strokes, for which the authors describe a method to sample the control points down to an amount usable by the robot. They also discuss how to create triangle strips for rendering a stroke digitally. The core of their paper considers the issue of using the iiwa's ability for compliant motions to paint on unknown surfaces. For this purpose, the robot obtains initial data about the target surface by probing certain locations with its tool, which allows the computation of a quad-tree height map. During painting, the heightmap is refined as the robot paints over more locations and detects any encountered resistance. Results are shown for paintings on cylindrical surfaces, sections of a sphere, and water containers of irregular shape. The system by [PERSON] et al. [13] requires firm pens to be able to sense the underlying surface and cannot use soft brushes. [PERSON] et al. [14] successfully created a learning system that generates painterly renderings using inverse reinforcement learning. They capture video of humans creating oriental ink paintings and use it to train a learning agent which can then reconstruct the movements of the brush footprint. They use boundary maps to extract shapes from a photograph, which the agent then attempts to fill optimally by generating a sequence of brush movements. Using a renderer, they can convert photographs into ink paintings. The approach by [PERSON] et al. [14] therefore mainly focuses on the creation of realistic single strokes, as opposed to other methods, which focus on filling in an entire painting with many simple strokes. One such method is provided by [PERSON] et al. [15], who use model-based Deep Reinforcement Learning agents to render images in a painterly style. Their renderer uses Bezier curves of varying thickness, color, and transparency.