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

arXiv: https://arxiv.org/abs/2311.01267 · HTML: https://mlsanity.com/paper/2311.01267/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [Reward learning from human preferences and demonstrations in Atari](https://mlsanity.com/paper/1811.06521/) (2018)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids](https://mlsanity.com/paper/1810.01566/) (2018)
- [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019)
- [CLOTH3D: Clothed 3D Humans](https://mlsanity.com/paper/1912.02792/) (2019)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning Visible Connectivity Dynamics for Cloth Smoothing](https://mlsanity.com/paper/2105.10389/) (2021)
- [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021)
- [SpeedFolding: Learning Efficient Bimanual Folding of Garments](https://mlsanity.com/paper/2208.10552/) (2022)
- [VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation](https://mlsanity.com/paper/2003.09044/) (2020)
- [Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation](https://mlsanity.com/paper/2206.02881/) (2022)
- [Analyzing the Variety Loss in the Context of Probabilistic Trajectory Prediction](https://mlsanity.com/paper/1907.10178/) (2019)
- [Cloth Funnels: Canonicalized-Alignment for Multi-Purpose Garment Manipulation](https://mlsanity.com/paper/2210.09347/) (2022)
- [Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience](https://mlsanity.com/paper/2010.03209/) (2020)
- [DextAIRity: Deformable Manipulation Can be a Breeze](https://mlsanity.com/paper/2203.01197/) (2022)
- [GarmentTracking: Category-Level Garment Pose Tracking](https://mlsanity.com/paper/2303.13913/) (2023)
- [FabricFolding: Learning Efficient Fabric Folding without Expert Demonstrations](https://mlsanity.com/paper/2303.06587/) (2023)
- [Learning Cloth Folding Tasks with Refined Flow Based Spatio-Temporal Graphs](https://mlsanity.com/paper/2110.08620/) (2021)

## Built on by (most cited first, 13 of 13)

- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [GarmentLab: A Unified Simulation and Benchmark for Garment Manipulation](https://mlsanity.com/paper/2411.01200/) (2024)
- [GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation](https://mlsanity.com/paper/2503.09243/) (2025)
- [DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment](https://mlsanity.com/paper/2410.11584/) (2024)
- [BiFold: Bimanual Cloth Folding with Language Guidance](https://mlsanity.com/paper/2501.16458/) (2025)
- [One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes](https://mlsanity.com/paper/2503.01092/) (2025)
- [LeHome: A Simulation Environment for Deformable Object Manipulation in Household Scenarios](https://mlsanity.com/paper/2604.22363/) (2026)
- [Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2606.04269/) (2026)
- [FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis](https://mlsanity.com/paper/2505.09109/) (2025)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [GarmentPile++: Affordance-Driven Cluttered Garments Retrieval with Vision-Language Reasoning](https://mlsanity.com/paper/2603.04158/) (2026)
- [FCBV-Net: Category-Level Robotic Garment Smoothing via Feature-Conditioned Bimanual Value Prediction](https://mlsanity.com/paper/2508.05153/) (2025)
- [Learning-based Cooperative Robotic Paper Wrapping: A Unified Control Policy with Residual Force Control](https://mlsanity.com/paper/2511.03181/) (2025)

## Abstract (arXiv)

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