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
this https URL. Video available at
this https URL.

本文利用深度学习和神经元元胞自动机，提出了一种可在硬件上成功应用的模块化 2D 机器人系统，该系统可以通过其组件的本地通信推断其自身的形状类别，实现了自分类的能力。