This paper explores the development of UniFolding, a sample-efficient, scalable, and generalizable robotic system for unfolding and folding various garments. UniFolding employs the proposed UFONet neural network to integrate unfolding and folding decisions into a single policy model that is adaptable to different garment types and states. The design of UniFolding is based on a garment's partial point cloud, which aids in generalization and reduces sensitivity to variations in texture and shape. The training pipeline prioritizes low-cost, sample-efficient data collection. Training data is collected via a human-centric process with offline and online stages. The offline stage involves human unfolding and folding actions via Virtual Reality, while the online stage utilizes human-in-the-loop learning to fine-tune the model 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 with significant variations in textures, shapes, and materials. More experiments and videos can be found in the supplementary materials and on the website: https://unifolding.robotflow.ai

翻译：本文探索了UniFolding系统的研发，该系统是一种样本高效、可扩展且通用化的机器人系统，用于对多种衣物进行展开与折叠操作。UniFolding采用所提出的UFONet神经网络，将展开与折叠决策整合至单一策略模型中，该模型可适应不同衣物类型与状态。UniFolding的设计基于衣物局部点云数据，有助于提升通用化能力并降低对纹理与形状变化的敏感性。其训练流程优先采用低成本、样本高效的数据收集方式。训练数据通过包含离线与在线阶段的人机协同流程采集：离线阶段通过虚拟现实采集人类的展开与折叠动作，在线阶段则利用人在回路学习在真实环境中对模型进行微调。系统在长袖与短袖两类衬衫上进行测试，性能评估涵盖20件纹理、形状及材质差异显著的衬衫。更多实验与视频详见补充材料及网站：https://unifolding.robotflow.ai