# GPT-Fabric: Smoothing and Folding Fabric by Leveraging Pre-Trained Foundation Models

arXiv: https://arxiv.org/abs/2406.09640 · HTML: https://mlsanity.com/paper/2406.09640/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation](https://mlsanity.com/paper/2503.09243/) (2025)
- [BiFold: Bimanual Cloth Folding with Language Guidance](https://mlsanity.com/paper/2501.16458/) (2025)
- [Fidelity-Aware Data Composition for Robust Robot Generalization](https://mlsanity.com/paper/2509.24797/) (2025)

## Abstract (arXiv)

Fabric manipulation has applications in folding blankets, handling patient clothing, and protecting items with covers. It is challenging for robots to perform fabric manipulation since fabrics have infinite-dimensional configuration spaces, complex dynamics, and may be in folded or crumpled configurations with severe self-occlusions. Prior work on robotic fabric manipulation relies either on heavily engineered setups or learning-based approaches that create and train on robot-fabric interaction data. In this paper, we propose GPT-Fabric for the canonical tasks of fabric smoothing and folding, where GPT directly outputs an action informing a robot where to grasp and pull a fabric. We perform extensive experiments in simulation to test GPT-Fabric against prior methods for smoothing and folding. GPT-Fabric matches the state-of-the-art in fabric smoothing, and also achieves comparable performance with most prior fabric folding methods tested, even without explicitly training on a fabric-specific dataset (i.e., zero-shot manipulation). Furthermore, we apply GPT-Fabric in physical experiments over 10 smoothing and 12 folding rollouts. Our results suggest that GPT-Fabric is a promising approach for high-precision fabric manipulation tasks