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Real-Time Grasp Detection Using Convolutional Neural Networks

arXiv 1412.3128 ↗First posted 9 Dec 2014cs.RO cs.CVcited by 76

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Abstract (arXiv)

We present an accurate, real-time approach to robotic grasp detection based on convolutional neural networks. Our network performs single-stage regression to graspable bounding boxes without using standard sliding window or region proposal techniques. The model outperforms state-of-the-art approaches by 14 percentage points and runs at 13 frames per second on a GPU. Our network can simultaneously perform classification so that in a single step it recognizes the object and finds a good grasp rectangle. A modification to this model predicts multiple grasps per object by using a locally constrained prediction mechanism. The locally constrained model performs significantly better, especially on objects that can be grasped in a variety of ways.

Key facts. Cited by 76 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Dec 2014.

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Citing papers per year: 2019: 1; 2020: 0; 2021: 22; 2022: 17; 2023: 13; 2024: 14; 2025: 7; 2026: 2.

76 papers.

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  1. 2022IMos: Intent-Driven Full-Body Motion Synthesis for Human-Object Interactionscited by 124
  2. 2021Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objectscited by 119
  3. 2022DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Videocited by 89
  4. 2024GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Graspingcited by 78
  5. 2023Bird's-Eye-View Scene Graph for Vision-Language Navigationcited by 72
  6. 2021AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactionscited by 57
  7. 2023NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesiscited by 57
  8. 2024Graspness Discovery in Clutters for Fast and Accurate Grasp Detectioncited by 48
  9. 2021O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learningcited by 46
  10. 2021End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGBcited by 44
  11. 2024GraspSplats: Efficient Manipulation with 3D Feature Splattingcited by 41
  12. 2021RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Imagescited by 39
38 more
  1. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 35
  2. 2021INVIGORATE: Interactive Visual Grounding and Grasping in Cluttercited by 32
  3. 2022MonoGraspNet: 6-DoF Grasping with a Single RGB Imagecited by 24
  4. 2021A Joint Network for Grasp Detection Conditioned on Natural Language Commandscited by 23
  5. 2024An Economic Framework for 6-DoF Grasp Detectioncited by 23
  6. 2021DemoGrasp: Few-Shot Learning for Robotic Grasping with Human Demonstrationcited by 20
  7. 2022DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulationcited by 18
  8. 2024Language-Driven 6-DoF Grasp Detection Using Negative Prompt Guidancecited by 16
  9. 2023Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methodscited by 15
  10. 2021Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimationcited by 14
  11. 20233DSGrasp: 3D Shape-Completion for Robotic Graspcited by 13
  12. 2022When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detectioncited by 12
  13. 2021REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Cluttercited by 11
  14. 2022DA2 Dataset: Toward Dexterity-Aware Dual-Arm Graspingcited by 11
  15. 2021IFR-Explore: Learning Inter-object Functional Relationships in 3D Indoor Scenescited by 9
  16. 2023RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attentioncited by 9
  17. 2024Lightweight Language-driven Grasp Detection using Conditional Consistency Modelcited by 9
  18. 2021GKNet: grasp keypoint network for grasp candidates detectioncited by 8
  19. 2023Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmarkcited by 7
  20. 2021Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenescited by 6
  21. 2021A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitivescited by 6
  22. 2022Visual-tactile Fusion for Transparent Object Grasping in Complex Backgroundscited by 5
  23. 2021HANDS: A Multimodal Dataset for Modeling Towards Human Grasp Intent Inference in Prosthetic Handscited by 4
  24. 2022I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketchescited by 4
  25. 2022Hybrid Physical Metric For 6-DoF Grasp Pose Detectioncited by 4
  26. 2023Fine-Tuning Generative Models as an Inference Method for Robotic Taskscited by 4
  27. 2023GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenescited by 4
  28. 2021Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Taskscited by 3
  29. 2022Open Arms: Open-Source Arms, Hands & Controlcited by 3
  30. 2022PoseIt: A Visual-Tactile Dataset of Holding Poses for Grasp Stability Analysiscited by 3
  31. 2023NBMOD: Find It and Grasp It in Noisy Backgroundcited by 3
  32. 2024Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networkscited by 3
  33. 2024GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learningcited by 3
  34. 2024Bimanual Grasp Synthesis for Dexterous Robot Handscited by 3
  35. 2021Double-Dot Network for Antipodal Grasp Detectioncited by 2
  36. 2022A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutionscited by 2
  37. 2024AGILE: Approach-based Grasp Inference Learned from Element Decompositioncited by 2
  38. 2024HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environmentscited by 2

50 most cited of 76 All 76 (JSON)