mlsanityData to 14 Oct 2026

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

arXiv 2205.06806 ↗First posted 25 Apr 2022cs.NE cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 25 Apr 2022.

Builds on

10 of 24 references.

References · 10

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  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
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  5. 2021The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learningcited by 50
  6. 2021Growing 3D Artefacts and Functional Machines with Neural Cellular Automatacited by 38
  7. 2021Illuminating Diverse Neural Cellular Automata for Level Generationcited by 35
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  9. 2022Variational Neural Cellular Automatacited by 28
  10. 2022Physical Neural Cellular Automata for 2D Shape Classificationcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.