mlsanityData to 7 Oct 2026

Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes

arXiv 2103.14127 ↗First posted 25 Mar 2021cs.RO cs.CVcited by 213

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Abstract (arXiv)

Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for closed-loop grasping. Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts. By rooting the full 6-DoF grasp pose and width in the observed point cloud, we can reduce the dimensionality of our grasp representation to 4-DoF which greatly facilitates the learning process. Our class-agnostic approach is trained on 17 million simulated grasps and generalizes well to real world sensor data. In a robotic grasping study of unseen objects in structured clutter we achieve over 90% success rate, cutting the failure rate in half compared to a recent state-of-the-art method.

Key facts. Cited by 213 papers in the map (data through 7 Oct 2026). Builds on 28 of its 37 references that are in the map. First posted on arXiv 25 Mar 2021.

Builds on

28 of 37 references.

References · 28

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  5. 2019DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusioncited by 352
  6. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
  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. 2019Implicit 3D Orientation Learning for 6D Object Detection from RGB Imagescited by 170
  10. 2019kPAM: KeyPoint Affordances for Category-Level Robotic Manipulationcited by 152
  11. 2018Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learningcited by 121
  12. 2020ACRONYM: A Large-Scale Grasp Dataset Based on Simulationcited by 115
16 more
  1. 2013Deep Learning for Detecting Robotic Graspscited by 114
  2. 2017Grasp Pose Detection in Point Cloudscited by 107
  3. 2019Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrationscited by 104
  4. 2019Self-supervised 6D Object Pose Estimation for Robot Manipulationcited by 100
  5. 2021Volumetric Grasping Network: Real-time 6 DOF Grasp Detection in Cluttercited by 94
  6. 2019PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Trackingcited by 91
  7. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
  8. 20196-DOF Grasping for Target-driven Object Manipulation in Cluttercited by 70
  9. 2020Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentationcited by 64
  10. 2019S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenescited by 51
  11. 2018Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervisioncited by 50
  12. 2020Object Rearrangement Using Learned Implicit Collision Functionscited by 43
  13. 2019The Best of Both Modes: Separately Leveraging RGB and Depth for Unseen Object Instance Segmentationcited by 39
  14. 2020PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Cloudscited by 39
  15. 2020Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivitycited by 15
  16. 2019Data-Efficient Learning for Sim-to-Real Robotic Grasping using Deep Point Cloud Prediction Networkscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 26; 2023: 52; 2024: 56; 2025: 40; 2026: 29.

213 papers.

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  1. 2022ProgPrompt: Generating Situated Robot Task Plans using Large Language Modelscited by 598
  2. 2023ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skillscited by 313
  3. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  4. 2024RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Roboticscited by 242
  5. 2023Learning to Act from Actionless Videos through Dense Correspondencescited by 159
  6. 2022DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulationcited by 129
  7. 2024THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulationcited by 128
  8. 2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 125
  9. 2021Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objectscited by 119
  10. 2023UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policycited by 117
  11. 2023HomeRobot: Open-Vocabulary Mobile Manipulationcited by 104
  12. 2023UniDexGrasp++: Improving Dexterous Grasping Policy Learning via Geometry-aware Curriculum and Iterative Generalist-Specialist Learningcited by 101
38 more
  1. 2021A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Executioncited by 91
  2. 2023LEAP Hand: Low-Cost, Efficient, and Anthropomorphic Hand for Robot Learningcited by 78
  3. 2024GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Graspingcited by 77
  4. 2022GraspNeRF: Multiview-based 6-DoF Grasp Detection for Transparent and Specular Objects Using Generalizable NeRFcited by 64
  5. 2023PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulationcited by 56
  6. 2022TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baselinecited by 55
  7. 2022Motion Policy Networkscited by 53
  8. 2022SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fieldscited by 51
  9. 2024Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Taskscited by 51
  10. 2021CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulationcited by 49
  11. 2022StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objectscited by 49
  12. 2021Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modelingcited by 43
  13. 2024Robot Utility Models: General Policies for Zero-Shot Deployment in New Environmentscited by 43
  14. 2022IFOR: Iterative Flow Minimization for Robotic Object Rearrangementcited by 42
  15. 2023When Prompt-based Incremental Learning Does Not Meet Strong Pretrainingcited by 41
  16. 2024GraspSplats: Efficient Manipulation with 3D Feature Splattingcited by 41
  17. 2024DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenescited by 41
  18. 2023PartManip: Learning Cross-Category Generalizable Part Manipulation Policy from Point Cloud Observationscited by 40
  19. 2023M2T2: Multi-Task Masked Transformer for Object-centric Pick and Placecited by 40
  20. 2024Closed-Loop Open-Vocabulary Mobile Manipulation with GPT-4Vcited by 40
  21. 2024RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulationcited by 40
  22. 2023KITE: Keypoint-Conditioned Policies for Semantic Manipulationcited by 39
  23. 2024Learning Precise Affordances from Egocentric Videos for Robotic Manipulationcited by 34
  24. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 34
  25. 2025MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulationcited by 34
  26. 2021NeRP: Neural Rearrangement Planning for Unknown Objectscited by 33
  27. 2023A Joint Modeling of Vision-Language-Action for Target-oriented Grasping in Cluttercited by 33
  28. 2024OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Roboticscited by 33
  29. 2025GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Trainingcited by 32
  30. 2022Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Cloudscited by 31
  31. 2023Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangementcited by 29
  32. 2024CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordancecited by 29
  33. 2025What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Modelscited by 29
  34. 2023Human-in-the-Loop Task and Motion Planning for Imitation Learningcited by 28
  35. 2024DITTO: Demonstration Imitation by Trajectory Transformationcited by 28
  36. 2023HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulationcited by 27
  37. 2024SUGAR: Pre-training 3D Visual Representations for Roboticscited by 26
  38. 2024M2Diffuser: Diffusion-based Trajectory Optimization for Mobile Manipulation in 3D Scenescited by 26

50 most cited of 213 All 213 (JSON)