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This paper explores the development of UniFolding, a sample-efficient,scalable, and generalizable robotic system for unfolding and folding variousgarments. UniFolding employs the proposed UFONet neural network to integrateunfolding and folding decisions into a single policy model that is adaptable todifferent garment types and states. The design of UniFolding is based on agarment's partial point cloud, which aids in generalization and reducessensitivity to variations in texture and shape. The training pipelineprioritizes low-cost, sample-efficient data collection. Training data iscollected via a human-centric process with offline and online stages. Theoffline stage involves human unfolding and folding actions via Virtual Reality,while the online stage utilizes human-in-the-loop learning to fine-tune themodel in a real-world setting. The system is tested on two garment types:long-sleeve and short-sleeve shirts. Performance is evaluated on 20 shirts withsignificant variations in textures, shapes, and materials. More experiments andvideos can be found in the supplementary materials and on the website:https://unifolding.robotflow.ai

UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding