# Learning a General Model: Folding Clothing with Topological Dynamics

arXiv: https://arxiv.org/abs/2504.20720 · HTML: https://mlsanity.com/paper/2504.20720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://mlsanity.com/paper/2107.06278/) (2021)
- [Learning to Simulate Complex Physics with Graph Networks](https://mlsanity.com/paper/2002.09405/) (2020)
- [ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://mlsanity.com/paper/2204.12484/) (2022)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [Learning to Manipulate Deformable Objects without Demonstrations](https://mlsanity.com/paper/1910.13439/) (2019)
- [GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completion](https://mlsanity.com/paper/2104.05177/) (2021)
- [Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation](https://mlsanity.com/paper/2003.08974/) (2020)
- [Folding Deformable Objects using Predictive Simulation and Trajectory Optimization](https://mlsanity.com/paper/1512.06922/) (2015)
- [VisuoSpatial Foresight for Physical Sequential Fabric Manipulation](https://mlsanity.com/paper/2102.09754/) (2021)
- [GarmentTracking: Category-Level Garment Pose Tracking](https://mlsanity.com/paper/2303.13913/) (2023)
- [Learning Visual Feedback Control for Dynamic Cloth Folding](https://mlsanity.com/paper/2109.04771/) (2021)
- [AdaFold: Adapting Folding Trajectories of Cloths via Feedback-loop Manipulation](https://mlsanity.com/paper/2403.06210/) (2024)

## Built on by (most cited first, 1 of 1)

- [Learn2Fold: Structured Origami Generation with World Model Planning](https://mlsanity.com/paper/2603.29585/) (2026)

## Abstract (arXiv)

The high degrees of freedom and complex structure of garments present significant challenges for clothing manipulation. In this paper, we propose a general topological dynamics model to fold complex clothing. By utilizing the visible folding structure as the topological skeleton, we design a novel topological graph to represent the clothing state. This topological graph is low-dimensional and applied for complex clothing in various folding states. It indicates the constraints of clothing and enables predictions regarding clothing movement. To extract graphs from self-occlusion, we apply semantic segmentation to analyze the occlusion relationships and decompose the clothing structure. The decomposed structure is then combined with keypoint detection to generate the topological graph. To analyze the behavior of the topological graph, we employ an improved Graph Neural Network (GNN) to learn the general dynamics. The GNN model can predict the deformation of clothing and is employed to calculate the deformation Jacobi matrix for control. Experiments using jackets validate the algorithm's effectiveness to recognize and fold complex clothing with self-occlusion.