用混合模型实现形态生成中的集中控制与自组织平衡。
Balancing Centralized Learning and Distributed Self-Organization: A Hybrid Model for Embodied Morphogenesis
- 将卷积控制器与可微分反应-扩散系统结合，动态调节参数以引导自组织。
- 混合模式在165步内实现100%严格收敛，控制成本比纯神经网络低15倍以上。
- 适合研究形态计算、自组织系统及生物启发式设计的学者参考。
背景：具身智能与发育形态发生都依赖于集中式指导与分布式物质动力学之间的分工，但调控自组织所需的顶层控制程度尚不明确。方法：我们将一个紧凑的全分辨率卷积控制器与可微分的灰-斯科特反应-扩散（RD）底物耦合，控制器观测U和V两个场，并对输入和消亡参数（ΔF和ΔK）施加平滑的增益调度调制。我们比较了纯RD、神经网络（NN）主导和混合三种模式，评估了谱选择性、收敛性与控制成本。结果：混合模式在约165步内实现100%严格收敛，而纯RD与NN主导基线在此时间内未收敛；其保持了底物的谱选择性，同时使用的L1努力仅约为NN主导控制器的1/15，L2功率降低超过200倍。中等振幅（A ≈ 0.03–0.045）形成黄金区域，在94–96步内实现100%准收敛。结论：有效控制应为‘播种后放手’：短暂、平滑的参数级微调将系统置入有利吸引盆，之后反应-扩散动力学自行完成并稳定图案。这为可控自组织提供了定量形态计算模型。
原文摘要 · Abstract (English)
Background: both embodied intelligence and developmental morphogenesis depend on a division of labour between centralized guidance and distributed material dynamics, but the amount of top-down control needed to steer self-organization remains unclear. Methods: we coupled a compact full-resolution convolutional controller to a differentiable Gray-Scott reaction-diffusion (RD) substrate. The controller observes two fields, U and V, and applies smooth gain-scheduled modulations of the feed and kill parameters (Delta F and Delta K). We compared pure RD, neural network (NN)-dominant and hybrid regimes and evaluated spectral selectivity, convergence and control cost. Results: the hybrid regime achieved 100% strict convergence at approximately 165 steps, whereas pure RD and the NN-dominant baseline did not converge within the same horizon. It matched the substrate's spectral selectivity while using approximately 15 times less L1 effort and over 200 times less L2 power than the NN-dominant controller. Moderate amplitudes (A approximately 0.03-0.045) formed a Goldilocks zone with 100% quasi-convergence in 94-96 steps. Conclusions: effective control is best framed as seed then cede: brief, smooth parameter-level nudges place the system in a favourable basin of attraction, after which reaction-diffusion dynamics complete and stabilize the pattern. This provides a quantitative model of morphological computation for controlled self-organization.
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