Berg Palm, Rasmus

Faina, Andres

Garcia, Rodrigo Moreno

Risi, Sebastian

Støy, Kasper

Walker, Kathryn Elizabeth

English

Materials with the ability to self-classify their own shape have the potential to advance a wide range of engineering applications and industries. Biological systems possess the ability not only to self-reconfigure but also to self-classify themselves to determine a general shape and function. Previous work into modular robotics systems has only enabled self-recognition and self-reconfiguration into a specific target shape, missing the inherent robustness present in nature to self-classify. In this paper we therefore take advantage of recent advances in deep learning and neural cellular automata, and present a simple modular 2D robotic system that can infer its own class of shape through the local communication of its components. Furthermore, we show that our system can be successfully transferred to hardware which thus opens opportunities for future self-classifying machines. Code available at https://github.com/kattwalker/projectcube. Video available at https://youtu.be/0TCOkE4keyc

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Physical Neural Cellular Automata for 2D Shape Classification

https://pure.itu.dk/files/90884499/project_cube_for_IROS_2022_5.pdf

Physical Neural Cellular Automata for 2D Shape Classification

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The IT University of Copenhagen's Repository