Fig. 3.

Supervised learning of cap-like force distributions. (A) We define distributions $S^{\text{dog}}$ (blue) and $S^{\text{cat}}$ (orange) of force patterns in the space of applied forces (2D projection shown). Twenty training examples (diamonds) are drawn from both distributions. (B) An untrained sheet folds into many distinct folded structures (different colors) in response to applied force patterns. As training progresses, most force patterns are classified as either blue or orange, according to the cap they belong to. When overtrained, all force patterns result in only one folded structure (orange). (C) The trained sheet reaches peak performance after $\sim 40$ epochs of supervised training (i.e., passes through the training examples). The trained sheet not only classifies the training set correctly (training accuracy), but generalizes to unseen test force patterns (test accuracy). (D) The trained sheet is highly accurate when classifying force patterns near the center of the distributions, but less accurate close to the true decision boundary between the distributions.