Learning to self-fold at a bifurcation

TL;DR

This paper demonstrates how disordered sheets can be physically trained to alter their folding pathways at bifurcation points through changes in crease stiffness induced by prior folding, enabling learning of non-linear behaviors.

Contribution

It introduces a physical training framework that modifies folding pathway topology via material plasticity, offering an alternative to computational design methods.

Findings

Training alters folding pathways in sheets through stiffness changes.

Different learning rules affect the robustness of pathway modifications.

Experimental validation with epoxy-filled creases shows successful pathway training.

Abstract

Disordered mechanical systems can deform along a network of pathways that branch and recombine at special configurations called bifurcation points. Multiple pathways are accessible from these bifurcation points; consequently, computer-aided design algorithms have been sought to achieve a specific structure of pathways at bifurcations by rationally designing the geometry and material properties of these systems. Here, we explore an alternative physical training framework in which the topology of folding pathways in a disordered sheet is changed in a desired manner due to changes in crease stiffnesses induced by prior folding. We study the quality and robustness of such training for different `learning rules', that is, different quantitative ways in which local strain changes the local folding stiffness. We experimentally demonstrate these ideas using sheets with epoxy-filled creases whose stiffnesses change due to folding before the epoxy sets. Our work shows how specific forms of plasticity in materials enable them to learn non-linear behaviors through their prior deformation history in a robust manner.