Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems

• Main Authors: Sudhakaran, Shyam, Najarro, Elias, Risi, Sebastian
• Format: Journal Article
• Language: English
• Published: 25-04-2022
• Subjects: Computer Science - Learning; Computer Science - Neural and Evolutionary Computing
• DOI: 10.48550/arxiv.2205.06806

Inspired by cellular growth and self-organization, Neural Cellular Automata
(NCAs) have been capable of "growing" artificial cells into images, 3D
structures, and even functional machines. NCAs are flexible and robust
computational systems but -- similarly to many other self-organizing systems --
inherently uncontrollable during and after their growth process. We present an
approach to control these type of systems called Goal-Guided Neural Cellular
Automata (GoalNCA), which leverages goal encodings to control cell behavior
dynamically at every step of cellular growth. This approach enables the NCA to
continually change behavior, and in some cases, generalize its behavior to
unseen scenarios. We also demonstrate the robustness of the NCA with its
ability to preserve task performance, even when only a portion of cells receive
goal information.