论文标题
目标引导的神经细胞自动机：学习控制自组织系统
Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems
论文作者
论文摘要
受细胞生长和自组织的启发，神经细胞自动机（NCAS）能够将人造细胞“生长”到图像，3D结构甚至功能机器中。 NCAS是灵活且健壮的计算系统，但与许多其他自组织系统类似 - 在其增长过程中和之后固有地无法控制。我们提出了一种控制这些类型的系统的方法，称为目标引导的神经细胞自动机（Goarsnca），该系统利用目标编码在细胞生长的每个步骤中动态控制细胞行为。这种方法使NCA能够不断改变行为，在某些情况下，将其行为推广到看不见的情况。我们还证明了NCA具有保持任务性能的能力的鲁棒性，即使只有一部分细胞收到目标信息。
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.
