# Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping

arXiv: https://arxiv.org/abs/2406.01767 · HTML: https://mlsanity.com/paper/2406.01767/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2024.

## Built on by (most cited first, 4 of 4)

- [GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training](https://mlsanity.com/paper/2507.13097/) (2025)
- [Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Clutter](https://mlsanity.com/paper/2505.02664/) (2025)
- [GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectors](https://mlsanity.com/paper/2608.00946/) (2026)
- [Vision-Based 6-DoF Grasp Pose Estimation for Robot Cloth Unfolding](https://mlsanity.com/paper/2609.31452/) (2026)

## Abstract (arXiv)

A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent, and the relationship between grasps and regional spaces remains incompletely investigated. In this paper, we propose Normalized Grasp Space (NGS) from a novel region-aware viewpoint, unifying the grasp representation within a normalized regional space and benefiting the generalizability of methods. Leveraging the NGS, we find that CNNs are underestimated for 3D feature extraction and 6-DoF grasp detection in clutter scenes and build a highly efficient Region-aware Normalized Grasp Network (RNGNet). Experiments on the public benchmark show that our method achieves significant >20% performance gains while attaining a real-time inference speed of approximately 50 FPS. Real-world cluttered scene clearance experiments underscore the effectiveness of our method. Further, human-to-robot handover and dynamic object grasping experiments demonstrate the potential of our proposed method for closed-loop grasping in dynamic scenarios.