  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.
