# A Virtual Reality Framework for Human-Robot Collaboration in Cloth Folding

arXiv: https://arxiv.org/abs/2305.07493 · HTML: https://mlsanity.com/paper/2305.07493/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2023.

## Built on by (most cited first, 7 of 7)

- [A Robotic Skill Learning System Built Upon Diffusion Policies and Foundation Models](https://mlsanity.com/paper/2403.16730/) (2024)
- [General-purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2408.08160/) (2024)
- [Real-Time Operator Takeover for Visuomotor Diffusion Policy Training](https://mlsanity.com/paper/2502.02308/) (2025)
- [Preference Aligned Visuomotor Diffusion Policies for Deformable Object Manipulation](https://mlsanity.com/paper/2602.09583/) (2026)
- [Flat'n'Fold: A Diverse Multi-Modal Dataset for Garment Perception and Manipulation](https://mlsanity.com/paper/2409.18297/) (2024)
- [CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints](https://mlsanity.com/paper/2507.19983/) (2025)
- [Hybrid Imitation Learning: Teleoperation Augmentation Primitives that Policies Learn to Trigger](https://mlsanity.com/paper/2512.04960/) (2025)

## Abstract (arXiv)

We present a virtual reality (VR) framework to automate the data collection process in cloth folding tasks. The framework uses skeleton representations to help the user define the folding plans for different classes of garments, allowing for replicating the folding on unseen items of the same class. We evaluate the framework in the context of automating garment folding tasks. A quantitative analysis is performed on 3 classes of garments, demonstrating that the framework reduces the need for intervention by the user. We also compare skeleton representations with RGB and binary images in a classification task on a large dataset of clothing items, motivating the use of the framework for other classes of garments.