Article

Distributed Swarm Morphogenesis with Neural Cellular Automata

The study proposes a modular framework that combines neural cellular automata (NCA) with existing site selection and collision-aware control for swarm robot morphogenesis. The framework introduces a decentralized attachment-site discovery mechanism in which robots already in place identify new attachment sites by updating and sharing local NCA states, eliminating the need for a predefined site list during deployment. The authors also propose offline sparse-retention fine-tuning and a false-positive loss to bridge NCA growth and gradual physical assembly. Simulations scale to several hundred robots and improve morphology accuracy, and experiments with Crazyflie quadrotors validate the integrated execution pipeline.

Part of the event: Release of a Distributed Cluster Morphogenesis Framework

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