# Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience

arXiv: https://arxiv.org/abs/2010.03209 · HTML: https://mlsanity.com/paper/2010.03209/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 24 of 24)

- [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)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation](https://mlsanity.com/paper/2303.11057/) (2023)
- [VoxAct-B: Voxel-Based Acting and Stabilizing Policy for Bimanual Manipulation](https://mlsanity.com/paper/2407.04152/) (2024)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects](https://mlsanity.com/paper/2104.12149/) (2021)
- [UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding](https://mlsanity.com/paper/2311.01267/) (2023)
- [Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies](https://mlsanity.com/paper/2107.08942/) (2021)
- [Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention](https://mlsanity.com/paper/2301.03003/) (2023)
- [Autonomously Untangling Long Cables](https://mlsanity.com/paper/2207.07813/) (2022)
- [GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation](https://mlsanity.com/paper/2503.09243/) (2025)
- [Disentangling Dense Multi-Cable Knots](https://mlsanity.com/paper/2106.02252/) (2021)
- [Learning Fabric Manipulation in the Real World with Human Videos](https://mlsanity.com/paper/2211.02832/) (2022)
- [SGTM 2.0: Autonomously Untangling Long Cables using Interactive Perception](https://mlsanity.com/paper/2209.13706/) (2022)
- [QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation](https://mlsanity.com/paper/2303.13320/) (2023)
- [Enabling Visual Action Planning for Object Manipulation through Latent Space Roadmap](https://mlsanity.com/paper/2103.02554/) (2021)
- [One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes](https://mlsanity.com/paper/2503.01092/) (2025)
- [Learning Vision-based Robotic Manipulation Tasks Sequentially in Offline Reinforcement Learning Settings](https://mlsanity.com/paper/2301.13450/) (2023)
- [PlaNet-ClothPick: Effective Fabric Flattening Based on Latent Dynamic Planning](https://mlsanity.com/paper/2303.01345/) (2023)
- [GarmentPile++: Affordance-Driven Cluttered Garments Retrieval with Vision-Language Reasoning](https://mlsanity.com/paper/2603.04158/) (2026)
- [Mixing Human Demonstrations with Self-Exploration in Experience Replay for Deep Reinforcement Learning](https://mlsanity.com/paper/2107.06840/) (2021)
- [DRAPER: Towards a Robust Robot Deployment and Reliable Evaluation for Quasi-Static Pick-and-Place Cloth-Shaping Neural Controllers](https://mlsanity.com/paper/2409.15159/) (2024)

## Abstract (arXiv)

Manipulating deformable objects, such as fabric, is a long standing problem in robotics, with state estimation and control posing a significant challenge for traditional methods. In this paper, we show that it is possible to learn fabric folding skills in only an hour of self-supervised real robot experience, without human supervision or simulation. Our approach relies on fully convolutional networks and the manipulation of visual inputs to exploit learned features, allowing us to create an expressive goal-conditioned pick and place policy that can be trained efficiently with real world robot data only. Folding skills are learned with only a sparse reward function and thus do not require reward function engineering, merely an image of the goal configuration. We demonstrate our method on a set of towel-folding tasks, and show that our approach is able to discover sequential folding strategies, purely from trial-and-error. We achieve state-of-the-art results without the need for demonstrations or simulation, used in prior approaches. Videos available at: https://sites.google.com/view/learningtofold