Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes
RL-Kirigami uses reinforcement learning to solve inverse design challenges in kirigami fabrication.
Kirigami fabrication faces challenges in mapping target shapes to valid cuts due to nonlinear deployment and discrete compatibility rules. RL-Kirigami combines optimal-transport conditional flow matching with reinforcement learning to generate feasible cut layouts for shape-programmable metamaterials.