# Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding

arXiv: https://arxiv.org/abs/2609.31452 · HTML: https://mlsanity.com/paper/2609.31452/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 48 references that are in the map.
- First posted on arXiv 25 Sep 2026.

## Builds on (most cited first, 27 of 27)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [GDRNPP: A Geometry-guided and Fully Learning-based Object Pose Estimator](https://mlsanity.com/paper/2102.12145/) (2021)
- [Learning Rich Features from RGB-D Images for Object Detection and Segmentation](https://mlsanity.com/paper/1407.5736/) (2014)
- [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021)
- [An Economic Framework for 6-DoF Grasp Detection](https://mlsanity.com/paper/2407.08366/) (2024)
- [Visuotactile Affordances for Cloth Manipulation with Local Control](https://mlsanity.com/paper/2212.05108/) (2022)
- [Unfolding the Literature: A Review of Robotic Cloth Manipulation](https://mlsanity.com/paper/2407.01361/) (2024)
- [Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidance](https://mlsanity.com/paper/2407.13842/) (2024)
- [ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping](https://mlsanity.com/paper/2504.10857/) (2025)
- [Learning to Grasp Clothing Structural Regions for Garment Manipulation Tasks](https://mlsanity.com/paper/2306.14553/) (2023)
- [Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics](https://mlsanity.com/paper/2303.01310/) (2023)
- [Learning Keypoints for Robotic Cloth Manipulation using Synthetic Data](https://mlsanity.com/paper/2401.01734/) (2024)
- [Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge](https://mlsanity.com/paper/2404.01727/) (2024)
- [DeformPAM: Data-Efficient Learning for Long-horizon Deformable Object Manipulation via Preference-based Action Alignment](https://mlsanity.com/paper/2410.11584/) (2024)
- [Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping](https://mlsanity.com/paper/2406.01767/) (2024)
- [BiFold: Bimanual Cloth Folding with Language Guidance](https://mlsanity.com/paper/2501.16458/) (2025)
- [Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance](https://mlsanity.com/paper/2509.03889/) (2025)
- [Dense Center-Direction Regression for Object Counting and Localization with Point Supervision](https://mlsanity.com/paper/2408.14457/) (2024)
- [Learning to unfold cloth: Scaling up world models to deformable object manipulation](https://mlsanity.com/paper/2602.16675/) (2026)
- [Center Direction Network for Grasping Point Localization on Cloths](https://mlsanity.com/paper/2408.14456/) (2024)
- [SIS: Seam-Informed Strategy for T-shirt Unfolding](https://mlsanity.com/paper/2409.06990/) (2024)
- [GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructions](https://mlsanity.com/paper/2503.16013/) (2025)
- [A Dataset and Benchmark for Robotic Cloth Unfolding Grasp Selection: The ICRA 2024 Cloth Competition](https://mlsanity.com/paper/2508.16749/) (2025)

## Abstract (arXiv)

Cloth manipulation is a challenging task due to the deformable and high-dimensional nature of cloth, which leads to complex interaction dynamics and perceptual ambiguity arising from frequent occlusions of critical visual cues such as folds, edges, and grasp points. In this work, we tackle cloth unfolding using a regrasping-in-the-air strategy, where one manipulator holds the cloth while the other grasps it at an optimally selected point to unfold it. To this end, we propose CeDiRNet-6DoF, a deep learning framework that jointly predicts effective grasp points and the complete 6-DoF grasp pose from the observed cloth configuration. By integrating dense 3D grasp regression with segmentation and sine-cosine-encoded Euler angles, the proposed method reliably estimates the grasp configuration that maximizes the unfolded cloth area. We extensively evaluated CeDiRNet-6DoF on a bimanual robotic setup within the ICRA 2024 Cloth Competition framework, achieving state-of-the-art performance. An ablation study further validates the benefits of key design components, including joint segmentation, background randomization, and image cropping. These results establish CeDiRNet-6DoF as a robust and versatile foundation for reliable robotic cloth manipulation in unstructured environments.