mlsanityData to 7 Oct 2026

Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection

arXiv 2211.00191 ↗First posted 31 Oct 2022cs.RO cs.AI cs.CVcited by 34

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Abstract (arXiv)

Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped. This important problem has many practical applications. Here we propose a novel method and neural network model that enables better grasp success rates relative to what is available in the literature. The method takes standard point cloud data as input and works well with single-view point clouds observed from arbitrary viewing directions.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). Builds on 24 of its 35 references that are in the map. First posted on arXiv 31 Oct 2022.

Builds on

24 of 35 references.

References · 24

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2018Open3D: A Modern Library for 3D Data Processingcited by 717
  5. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  6. 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 287
  7. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
  8. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 237
  9. 2021Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulationcited by 204
  10. 2018Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approachcited by 117
  11. 2013Deep Learning for Detecting Robotic Graspscited by 114
  12. 2017Grasp Pose Detection in Point Cloudscited by 107
12 more
  1. 2021Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representationscited by 107
  2. 2021Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Cluttercited by 94
  3. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
  4. 2021Equivariant Q Learning in Spatial Action Spacescited by 61
  5. 2019Antipodal Robotic Grasping using Generative Residual Convolutional Neural Networkcited by 59
  6. 2022Sample Efficient Grasp Learning Using Equivariant Modelscited by 52
  7. 2019S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenescited by 51
  8. 2018A Graph-CNN for 3D Point Cloud Classificationcited by 49
  9. 2022Equivariant Transporter Networkcited by 44
  10. 2016High precision grasp pose detection in dense cluttercited by 33
  11. 2020Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Graspscited by 22
  12. 2022GASCN: Graph Attention Shape Completion Networkcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 10; 2025: 15; 2026: 4.

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  1. 2024Equivariant Diffusion Policycited by 61
  2. 2023Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulationcited by 51
  3. 2025Diffusion Models for Robotic Manipulation: A Surveycited by 39
  4. 2024AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulationcited by 20
  5. 2023A General Theory of Correct, Incorrect, and Extrinsic Equivariancecited by 18
  6. 2023Leveraging Symmetries in Pick and Placecited by 18
  7. 2024Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policiescited by 18
  8. 2024ICGNet: A Unified Approach for Instance-Centric Graspingcited by 16
  9. 2023On Robot Grasp Learning Using Equivariant Modelscited by 12
  10. 2024RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentationcited by 12
  11. 2025Two by Two: Learning Multi-Task Pairwise Objects Assembly for Generalizable Robot Manipulationcited by 12
  12. 2025SE(3)-Equivariant Robot Learning and Control: A Tutorial Surveycited by 8
22 more
  1. 2025SE(3)-Equivariant Diffusion Policy in Spherical Fourier Spacecited by 7
  2. 2024EqvAfford: SE(3) Equivariance for Point-Level Affordance Learningcited by 6
  3. 2024MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policiescited by 6
  4. 2024SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimationcited by 6
  5. 2025Constraint-Preserving Data Generation for Visuomotor Policy Learningcited by 6
  6. 2023Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Renderingcited by 5
  7. 2024Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distancecited by 4
  8. 2025A Practical Guide for Incorporating Symmetry in Diffusion Policycited by 4
  9. 2025EfficientFlow: Efficient Equivariant Flow Policy Learning for Embodied AIcited by 4
  10. 2025Coarse-to-Fine 3D Keyframe Transportercited by 2
  11. 2025Generalizable Hierarchical Skill Learning via Object-Centric Representationcited by 2
  12. 2025Symmetries-enhanced Multi-Agent Reinforcement Learningcited by 1
  13. 2025Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematicscited by 1
  14. 2025MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Graspingcited by 1
  15. 2025Equivariant Volumetric Graspingcited by 1
  16. 2025On Uncertainty Calibration for Equivariant Functionscited by 1
  17. 2026EquiDexFlow: Contact-Grounded SE(3)-Equivariant Dexterous Grasp Generative Flowscited by 1
  18. 2024Robust Loss Functions for Object Grasping under Limited Ground Truthcited by 0
  19. 2025Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulationcited by 0
  20. 2026Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classificationcited by 0
  21. 2026Pix2Act: Image-Space Manipulation Policies with Equivariant Augmentationcited by 0
  22. 2026Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrationscited by 0