SpeedFolding: Learning Efficient Bimanual Folding of Garments

Abstract

Folding garments reliably and efficiently is a long standing challenge in robotic manipulation due to the complex dynamics and high dimensional configuration space of garments. An intuitive approach is to initially manipulate the garment to a canonical smooth configuration before folding. In this work, we develop SpeedFolding, a reliable and efficient bimanual system, which given user-defined instructions as folding lines, manipulates an initially crumpled garment to (1) a smoothed and (2) a folded configuration. Our primary contribution is a novel neural network architecture that is able to predict pairs of gripper poses to parameterize a diverse set of bimanual action primitives. After learning from 4300 human- annotated and self-supervised actions, the robot is able to fold garments from a random initial configuration in under 120 s on average with a success rate of 93 %. Real-world experiments show that the system is able to generalize to unseen garments of different color, shape, and stiffness. While prior work achieved 3–6 Folds Per Hour (FPH), SpeedFolding achieves 30–40 FPH. See https://pantor.github.io/speedfolding for code, videos, and datasets.