Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypes
Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult because deployment is nonlinear, and feasible cut layouts must satisfy discrete compatibility rules, avoid overlap, and map one target shape to valid designs. We present RL-Kirigami, an inverse design framework that combines optimal-transport conditional flow matching (OT-CFM) with reinforcement learning to generate compatible ratio fields for compact reconfigurable parallelogram quad kirigami. A marching decoder enforces global geometric compatibility, and Group Relative Policy Optimization (GRPO) aligns the generator with nondifferentiabl...
| Authors | Milad Yazdani, Shahriar Shalileh, Dena Shahriari |
|---|---|
| Primary category | cs.LG |
| Published | 16 April 2026 |
| Last updated at source | 8 October 2026 |
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