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

Han Xue, Yutong Li, Wenqiang Xu, Huanyu Li, Dongzhe Zheng, Cewu Lu
Proceedings of The 7th Conference on Robot Learning, PMLR 229:3321-3341, 2023.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v229-xue23b, title = {UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding}, author = {Xue, Han and Li, Yutong and Xu, Wenqiang and Li, Huanyu and Zheng, Dongzhe and Lu, Cewu}, booktitle = {Proceedings of The 7th Conference on Robot Learning}, pages = {3321--3341}, year = {2023}, editor = {Tan, Jie and Toussaint, Marc and Darvish, Kourosh}, volume = {229}, series = {Proceedings of Machine Learning Research}, month = {06--09 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v229/xue23b/xue23b.pdf}, url = {https://proceedings.mlr.press/v229/xue23b.html}, abstract = {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.} }
Endnote
%0 Conference Paper %T UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding %A Han Xue %A Yutong Li %A Wenqiang Xu %A Huanyu Li %A Dongzhe Zheng %A Cewu Lu %B Proceedings of The 7th Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2023 %E Jie Tan %E Marc Toussaint %E Kourosh Darvish %F pmlr-v229-xue23b %I PMLR %P 3321--3341 %U https://proceedings.mlr.press/v229/xue23b.html %V 229 %X 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.
APA
Xue, H., Li, Y., Xu, W., Li, H., Zheng, D. & Lu, C.. (2023). UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding. Proceedings of The 7th Conference on Robot Learning, in Proceedings of Machine Learning Research 229:3321-3341 Available from https://proceedings.mlr.press/v229/xue23b.html.

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