mlsanityData to 7 Oct 2026

GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes

arXiv 2312.03345 ↗First posted 6 Dec 2023cs.RO cs.CVcited by 4

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Abstract (arXiv)

6-DoF object-agnostic grasping in unstructured environments is a critical yet challenging task in robotics. Most current works use non-optimized approaches to sample grasp locations and learn spatial features without concerning the grasping task. This paper proposes GraNet, a graph-based grasp pose generation framework that translates a point cloud scene into multi-level graphs and propagates features through graph neural networks. By building graphs at the scene level, object level, and grasp point level, GraNet enhances feature embedding at multiple scales while progressively converging to the ideal grasping locations by learning. Our pipeline can thus characterize the spatial distribution of grasps in cluttered scenes, leading to a higher rate of effective grasping. Furthermore, we enhance the representation ability of scalable graph networks by a structure-aware attention mechanism to exploit local relations in graphs. Our method achieves state-of-the-art performance on the large-scale GraspNet-1Billion benchmark, especially in grasping unseen objects (+11.62 AP). The real robot experiment shows a high success rate in grasping scattered objects, verifying the effectiveness of the proposed approach in unstructured environments.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 34 of its 43 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

34 of 43 references.

References · 34

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  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
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  11. 2017Real-Time Seamless Single Shot 6D Object Pose Predictioncited by 210
  12. 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 208
22 more
  1. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 152
  2. 2018Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approachcited by 117
  3. 2017Grasp Pose Detection in Point Cloudscited by 107
  4. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
  5. 2014Real-Time Grasp Detection Using Convolutional Neural Networkscited by 75
  6. 2016Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challengecited by 70
  7. 20196-DOF Grasping for Target-driven Object Manipulation in Cluttercited by 70
  8. 2019Antipodal Robotic Grasping using Generative Residual Convolutional Neural Networkcited by 59
  9. 2019S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenescited by 51
  10. 2021CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulationcited by 49
  11. 2016Robotic Grasp Detection using Deep Convolutional Neural Networkscited by 47
  12. 2020PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Cloudscited by 39
  13. 2021RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Imagescited by 38
  14. 2017SegICP: Integrated Deep Semantic Segmentation and Pose Estimationcited by 29
  15. 2019TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensorscited by 15
  16. 2021Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimationcited by 14
  17. 2022End-to-End Learning to Grasp via Sampling from Object Point Cloudscited by 14
  18. 2019Learning to Generate 6-DoF Grasp Poses with Reachability Awarenesscited by 7
  19. 2022Learning Object Relations with Graph Neural Networks for Target-Driven Grasping in Dense Cluttercited by 6
  20. 2022Multi-Fingered In-Hand Manipulation with Various Object Properties Using Graph Convolutional Networks and Distributed Tactile Sensorscited by 5
  21. 2019Efficient Fully Convolution Neural Network for Generating Pixel Wise Robotic Grasps With High Resolution Imagescited by 4
  22. 2022Hybrid Physical Metric For 6-DoF Grasp Pose Detectioncited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024An Economic Framework for 6-DoF Grasp Detectioncited by 23
  2. 2025Grasp the Graph (GtG) 2.0: Ensemble of Graph Neural Networks for High-Precision Grasp Pose Detection in Cluttercited by 1
  3. 2025Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environmentscited by 0
  4. 2026GraRe: Grasp Candidate Re-Ranking for Frozen 6-DoF Grasp Detectorscited by 0