Abstract
Artificial Intelligence (AI) and robots impact creative jobs such as art-making. There have been many AI tools assisting average users in imitating the styles of renowned painters from the past. The Convolutional neural network (CNN) and generative adversarial network (GAN) emergence as a method to “hallucinate” and create expressions of styled drawings. This paper discussed an experiment to study how AI, Automation, and Robots (AAR) will interact with humans and form a unique symbiotic relationship in art-making. Our project, called “robot painter,” established a co-creation in calligraphy-style painting with the following steps: (1) Use CNN tools to translate a raster image into a calligraphy-style image. (2) Develop an algorithm in Grasshopper and Rhino program for the Kuka robot. This generative tool allowed the artist to translate the image into a parametrically controlled 3D toolpath for a robotic arm. (3) A KUKA robot executed the art-making by holding a paintbrush and completing the painting with customized stoke, force, and angle on a canvas.
In conclusion, the paper discussed that AAR makes human intervention and co-creation possible. The ability of A. I and robots to mimic artists’ expressions have undoubtedly achieved a convincing level and will affect art-making in the years to come.
Keywords
- Artificial intelligence
- Robot
- Art
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Notes
- 1.
KUKA prc builds upon the accessible visual programming system Grasshopper, which is a part of the CAD software Rhinoceros 3D. It provides the robotic building blocks to directly integrate a KUKA robot into a parametric environment. Instead of writing code, simple function-blocks are connected with each other and the results immediately visualized. https://www.robotsinarchitecture.org/kuka-prc.
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Tang, M. (2022). Human and Machine Symbiosis - An Experiment of Human and Robot Co-creation of Calligraphy-Style Drawing. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2022 Posters. HCII 2022. Communications in Computer and Information Science, vol 1580. Springer, Cham. https://doi.org/10.1007/978-3-031-06417-3_62
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