Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypes
The RL-Kirigami framework uses an optimal-transport conditional flow matching prior to generate kirigami cut patterns for compact quad structures, which are then refined by reinforcement learning. This method overcomes traditional slow inverse design by using a generative model that produces feasible designs quickly, allowing for rapid laser cutting prototypes.