FoldNet++: a Large-Scale Synthetic Dataset for Robotic T-Shirt Folding and Unfolding

2026-09-11

AI Summary

为解决机器人T恤折叠和展开难题，本文构建了一个大规模合成数据集FoldNet++，并通过基于规则的框架生成操作演示来训练视觉运动策略。

Abstract

Due to the highly deformable nature of garments, training a generalizable policy for robotic T-shirt folding and unfolding remains a significant challenge. In this work, we present a large-scale synthetic dataset for robotic T-shirt folding and unfolding, covering 6 robotic embodiments, 1K T-shirts, 1K environmental assets, and 120K episodes with rich annotations, which can be used to train a wide range of manipulation policies. We first follow the FoldNet pipeline to generate a large-scale dataset of physically simulatable T-shirts with diverse appearances and annotated semantic keypoints. Based on these semantic keypoints, we then generate manipulation demonstrations for different robotic embodiments through a unified rule-based framework. We use these demonstrations to train visuomotor policies, and experimental results demonstrate that models trained solely on our synthetic data can achieve over 90\% end-to-end task success rates when directly deployed to unseen real-world environments and previously unseen T-shirts from arbitrary initial configurations. Project URL: https://pku-epic.github.io/FoldNetXX/.

Problem

• garments
• deformable
• robotic T-shirt folding and unfolding
• generalizable policy

Innovation

• synthetic dataset
• robotic T-shirt manipulation
• rule-based framework
• generalizable policy