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arXiv Machine Learning By Milad Yazdani, Shahriar Shalileh, Dena Shahriari

Reinforcement learning for inverse silhouette design and rapid laser cutting of compact parallelogram quad kirigami prototypes

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The paper introduces RL‑Kirigami, an inverse design framework that uses optimal‑transport conditional flow matching (OT‑CFM) and reinforcement learning to generate compatible ratio fields for compact reconfigurable parallelogram quad kirigami. A marching decoder enforces global geometric compatibility, while Group Relative Policy Optimization (GRPO) aligns the generator with nondifferentiable rewards for silhouette matching, feasibility, and ratio‑field regularity. The method achieves high silhouette IoU scores (up to 94.91%) and reduces forward simulator evaluations from hundreds to one, enabling rapid laser‑cut prototypes in polymeric sheets within about eight minutes.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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