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Folding garments reliably and efficiently is a long standing challenge inrobotic manipulation due to the complex dynamics and high dimensionalconfiguration space of garments. An intuitive approach is to initiallymanipulate the garment to a canonical smooth configuration before folding. Inthis work, we develop SpeedFolding, a reliable and efficient bimanual system,which given user-defined instructions as folding lines, manipulates aninitially crumpled garment to (1) a smoothed and (2) a folded configuration.Our primary contribution is a novel neural network architecture that is able topredict pairs of gripper poses to parameterize a diverse set of bimanual actionprimitives. After learning from 4300 human-annotated and self-supervisedactions, the robot is able to fold garments from a random initial configurationin under 120s on average with a success rate of 93%. Real-world experimentsshow that the system is able to generalize to unseen garments of differentcolor, shape, and stiffness. While prior work achieved 3-6 Folds Per Hour(FPH), SpeedFolding achieves 30-40 FPH.

SpeedFolding: Learning Efficient Bimanual Folding of Garments