Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes

RL-Kirigami is a novel inverse design framework that combines optimal-transport conditional flow matching (OT-CFM) with reinforcement learning. This method generates compatible ratio fields for compact reconfigurable parallelogram quad kirigami, outperforming solver baselines in silhouette matching and design feasibility.