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arXiv cs.LG
arXiv cs.LG
5/12/2026
Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes

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

Short summary

Researchers developed RL-Kirigami, combining reinforcement learning with optimal-transport flow matching to design kirigami metamaterials under hard geometric constraints. The framework achieved 94.91% structural accuracy while generating laser-cuttable designs in under 8 minutes per prototype. Successfully fabricated deployable kirigami structures in polymeric sheets, validating a manufacturing-aware inverse design workflow.

  • •Novel RL-Kirigami framework integrates GRPO with optimal-transport conditional flow matching for constrained design
  • •Achieved 94.91% sIoU accuracy with rapid prototyping at 8 minutes per laser-cut part
  • •Demonstrated manufacturing-aware inverse design solving discrete compatibility and geometric feasibility constraints

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