# Learning Visual Feedback Control for Dynamic Cloth Folding

arXiv: https://arxiv.org/abs/2109.04771 · HTML: https://mlsanity.com/paper/2109.04771/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2021.

## Built on by (most cited first, 10 of 10)

- [Benchmarking the Sim-to-Real Gap in Cloth Manipulation](https://mlsanity.com/paper/2310.09543/) (2023)
- [Learning Robot Manipulation from Cross-Morphology Demonstration](https://mlsanity.com/paper/2304.03833/) (2023)
- [Cloth-Splatting: 3D Cloth State Estimation from RGB Supervision](https://mlsanity.com/paper/2501.01715/) (2025)
- [Learning Fabric Manipulation in the Real World with Human Videos](https://mlsanity.com/paper/2211.02832/) (2022)
- [QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation](https://mlsanity.com/paper/2303.13320/) (2023)
- [ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models](https://mlsanity.com/paper/2408.15919/) (2024)
- [Learning a General Model: Folding Clothing with Topological Dynamics](https://mlsanity.com/paper/2504.20720/) (2025)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [Dynamic Manipulation of Deformable Objects using Imitation Learning with Adaptation to Hardware Constraints](https://mlsanity.com/paper/2403.12685/) (2024)
- [Dynamic robotic cloth folding with efficient Koopman operator-based model predictive control](https://mlsanity.com/paper/2605.18373/) (2026)

## Abstract (arXiv)

Robotic manipulation of cloth is a challenging task due to the high dimensionality of the configuration space and the complexity of dynamics affected by various material properties. The effect of complex dynamics is even more pronounced in dynamic folding, for example, when a square piece of fabric is folded in two by a single manipulator. To account for the complexity and uncertainties, feedback of the cloth state using e.g. vision is typically needed. However, construction of visual feedback policies for dynamic cloth folding is an open problem. In this paper, we present a solution that learns policies in simulation using Reinforcement Learning (RL) and transfers the learned policies directly to the real world. In addition, to learn a single policy that manipulates multiple materials, we randomize the material properties in simulation. We evaluate the contributions of visual feedback and material randomization in real-world experiments. The experimental results demonstrate that the proposed solution can fold successfully different fabric types using dynamic manipulation in the real world. Code, data, and videos are available at https://sites.google.com/view/dynamic-cloth-folding