Physical Neural Cellular Automata for 2D Shape Classification

Kathryn Elizabeth Walker, Rasmus Berg Palm, Rodrigo Moreno Garcia, Andres Faina, Kasper Støy, Sebastian Risi

Publikation: Artikel i tidsskrift og konference artikel i tidsskriftKonferenceartikelForskningpeer review


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 Video available at
TidsskriftIROS 2022
Antal sider7
StatusUdgivet - 2022


Dyk ned i forskningsemnerne om 'Physical Neural Cellular Automata for 2D Shape Classification'. Sammen danner de et unikt fingeraftryk.