mlsanityData to 14 Oct 2026

SuctionNet-1Billion: A Large-Scale Benchmark for Suction Grasping

arXiv 2103.12311 ↗First posted 23 Mar 2021cs.RO cs.CVcited by 32

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Abstract (arXiv)

Suction is an important solution for the longstanding robotic grasping problem. Compared with other kinds of grasping, suction grasping is easier to represent and often more reliable in practice. Though preferred in many scenarios, it is not fully investigated and lacks sufficient training data and evaluation benchmarks. To address that, firstly, we propose a new physical model to analytically evaluate seal formation and wrench resistance of a suction grasping, which are two key aspects of grasp success. Secondly, a two-step methodology is adopted to generate annotations on a large-scale dataset collected in real-world cluttered scenarios. Thirdly, a standard online evaluation system is proposed to evaluate suction poses in continuous operation space, which can benchmark different algorithms fairly without the need of exhaustive labeling. Real-robot experiments are conducted to show that our annotations align well with real world. Meanwhile, we propose a method to predict numerous suction poses from an RGB-D image of a cluttered scene and demonstrate our superiority against several previous methods. Result analyses are further provided to help readers better understand the challenges in this area. Data and source code are publicly available at www.graspnet.net.

Key facts. Cited by 32 papers in the map (data through 14 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 23 Mar 2021.

Builds on

21 of 32 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  4. 2019Objects as Pointscited by 1,175
  5. 2017PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenescited by 775
  6. 2018Open3D: A Modern Library for 3D Data Processingcited by 721
  7. 2018PVNet: Pixel-wise Voting Network for 6DoF Pose Estimationcited by 385
  8. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 264
  9. 2019PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimationcited by 204
  10. 2017Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matchingcited by 125
  11. 2017Grasp Pose Detection in Point Cloudscited by 111
  12. 2018PointNetGPD: Detecting Grasp Configurations from Point Setscited by 80
9 more
  1. 2021RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Imagescited by 39
  2. 2015A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Placecited by 35
  3. 2019Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Pickingcited by 25
  4. 2018RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Cluttercited by 21
  5. 2017Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challengecited by 19
  6. 2018Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packingcited by 12
  7. 2016Team Delft's Robot Winner of the Amazon Picking Challenge 2016cited by 10
  8. 2019Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Cluttercited by 10
  9. 2018Estimating 6D Pose From Localizing Designated Surface Keypointscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 2; 2022: 5; 2023: 9; 2024: 3; 2025: 7; 2026: 6.

32 papers.

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  1. 2021Hand-Object Contact Consistency Reasoning for Human Grasps Generationcited by 160
  2. 2022End-to-End Affordance Learning for Robotic Manipulationcited by 57
  3. 2024Graspness Discovery in Clutters for Fast and Accurate Grasp Detectioncited by 48
  4. 20223D-Aware Object Goal Navigation via Simultaneous Exploration and Identificationcited by 46
  5. 2023Challenges for Monocular 6D Object Pose Estimation in Roboticscited by 21
  6. 2025Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomycited by 20
  7. 2022MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesiscited by 13
  8. 2023Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edgecited by 7
  9. 2023Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmarkcited by 7
  10. 2023LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusorcited by 7
  11. 2023Uncertainty-driven Exploration Strategies for Online Grasp Learningcited by 6
  12. 2026Data Pyramid for Embodied Manipulation: A Surveycited by 6
20 more
  1. 2021Learning suction graspability considering grasp quality and robot reachability for bin-pickingcited by 5
  2. 2023Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Pickingcited by 5
  3. 2022Mechanical Search on Shelves using a Novel "Bluction" Toolcited by 3
  4. 2023STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects on Production Linescited by 2
  5. 2024OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robotscited by 2
  6. 2025Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environmentscited by 2
  7. 2025Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Graspingcited by 2
  8. 2023Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptorscited by 1
  9. 2024Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Pickingcited by 1
  10. 2025Diffusion Suction Grasping with Large-Scale Parcel Datasetcited by 1
  11. 2025Learning to Optimize Package Picking for Large-Scale, Real-World Robot Inductioncited by 1
  12. 2025Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Successcited by 1
  13. 2022MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancycited by 0
  14. 2023Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environmentscited by 0
  15. 2025Placeit! A Framework for Learning Robot Object Placement Skillscited by 0
  16. 2026MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Graspingcited by 0
  17. 2026Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applicationscited by 0
  18. 2026Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Executioncited by 0
  19. 2026Seg2Grasp: A Robust Modular Suction Grasping in Bin Pickingcited by 0
  20. 2026Training-free Suction Grasp Detection for Deformed Aseptic Cartons Using Vision-Language Models and Geometric Surface Scoringcited by 0