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

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: this https URL.

Robotics (cs.RO)

arXiv:2609.12433 [cs.RO]

(or arXiv:2609.12433v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.12433 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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