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Robotic Grasp Detection using Deep Convolutional Neural Networks

arXiv 1611.08036 ↗First posted 24 Nov 2016cs.RO cs.CVcited by 47

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Abstract (arXiv)

Deep learning has significantly advanced computer vision and natural language processing. While there have been some successes in robotics using deep learning, it has not been widely adopted. In this paper, we present a novel robotic grasp detection system that predicts the best grasping pose of a parallel-plate robotic gripper for novel objects using the RGB-D image of the scene. The proposed model uses a deep convolutional neural network to extract features from the scene and then uses a shallow convolutional neural network to predict the grasp configuration for the object of interest. Our multi-modal model achieved an accuracy of 89.21% on the standard Cornell Grasp Dataset and runs at real-time speeds. This redefines the state-of-the-art for robotic grasp detection.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Nov 2016.

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Citing papers per year: 2021: 13; 2022: 14; 2023: 10; 2024: 6; 2025: 4.

47 papers.

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  1. 2023Open-World Object Manipulation using Pre-trained Vision-Language Modelscited by 155
  2. 2024Graspness Discovery in Clutters for Fast and Accurate Grasp Detectioncited by 47
  3. 2021RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Imagescited by 38
  4. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 34
  5. 2022MonoGraspNet: 6-DoF Grasping with a Single RGB Imagecited by 24
  6. 2021A Joint Network for Grasp Detection Conditioned on Natural Language Commandscited by 23
  7. 2023Open-sourced Data Ecosystem in Autonomous Driving: the Present and Futurecited by 22
  8. 2021Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimationcited by 14
  9. 2023Exploring Visual Pre-training for Robot Manipulation: Datasets, Models and Methodscited by 14
  10. 2022When Transformer Meets Robotic Grasping: Exploits Context for Efficient Grasp Detectioncited by 12
  11. 2021REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Cluttercited by 11
  12. 2022DA2 Dataset: Toward Dexterity-Aware Dual-Arm Graspingcited by 10
35 more
  1. 2023RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attentioncited by 9
  2. 2024Lightweight Language-driven Grasp Detection using Conditional Consistency Modelcited by 9
  3. 2021GKNet: grasp keypoint network for grasp candidates detectioncited by 8
  4. 2021A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitivescited by 6
  5. 2022Environment Optimization for Multi-Agent Navigationcited by 6
  6. 2023Constrained Environment Optimization for Prioritized Multi-Agent Navigationcited by 6
  7. 2021Learning suction graspability considering grasp quality and robot reachability for bin-pickingcited by 5
  8. 2022I Know What You Draw: Learning Grasp Detection Conditioned on a Few Freehand Sketchescited by 4
  9. 2022Hybrid Physical Metric For 6-DoF Grasp Pose Detectioncited by 4
  10. 2023GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenescited by 4
  11. 2021Depth-aware Object Segmentation and Grasp Detection for Robotic Picking Taskscited by 3
  12. 2022Open Arms: Open-Source Arms, Hands & Controlcited by 3
  13. 2023NBMOD: Find It and Grasp It in Noisy Backgroundcited by 3
  14. 2024Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networkscited by 3
  15. 2021Double-Dot Network for Antipodal Grasp Detectioncited by 2
  16. 2022A Robotic Visual Grasping Design: Rethinking Convolution Neural Network with High-Resolutionscited by 2
  17. 2023MotorFactory: A Blender Add-on for Large Dataset Generation of Small Electric Motorscited by 2
  18. 2024HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environmentscited by 2
  19. 2025VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generationcited by 2
  20. 2021Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detectioncited by 1
  21. 2021GI-NNet & RGI-NNet: Development of Robotic Grasp Pose Models, Trainable with Large as well as Limited Labelled Training Datasets, under supervised and semi supervised paradigmscited by 1
  22. 2023Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptorscited by 1
  23. 2023FViT-Grasp: Grasping Objects With Using Fast Vision Transformerscited by 1
  24. 2024Simple In-place Data Augmentation for Surveillance Object Detectioncited by 1
  25. 2025Visual Affordance Prediction: Survey and Reproducibilitycited by 1
  26. 2025Language-Guided Grasp Detection with Coarse-to-Fine Learning for Robotic Manipulationcited by 1
  27. 2021Real-World Semantic Grasp Detection Based on Attention Mechanismcited by 0
  28. 2021Development of a robust cascaded architecture for intelligent robot grasping using limited labelled datacited by 0
  29. 2022PEGG-Net: Pixel-Wise Efficient Grasp Generation in Complex Scenescited by 0
  30. 2022DcnnGrasp: Towards Accurate Grasp Pattern Recognition with Adaptive Regularizer Learningcited by 0
  31. 2022Evaluating Gaussian Grasp Maps for Generative Grasping Modelscited by 0
  32. 2022A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Armscited by 0
  33. 2022Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methodscited by 0
  34. 2022MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancycited by 0
  35. 2025XGrasp: Gripper-Aware Grasp Detection with Multi-Gripper Data Generationcited by 0