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Inverse Design of Origami for Trajectory Following Using Deep Learning

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This approach utilizes a variational autoencoder to optimize origami designs for trajectory following, suggesting efficient solutions.

Results show that the method efficiently generates large datasets of rigidly foldable origami patterns.
The variational autoencoder maps origami crease patterns into a continuous latent space for design sampling.
Utilizing normalizing flows enhances the reconstruction accuracy of the crease patterns in the latent space.
The learned representation allows efficient optimization of origami designs for various target trajectories.

Hochuli et al. (2025) studied this question.

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