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

arXiv: https://arxiv.org/abs/2103.12311 · HTML: https://mlsanity.com/paper/2103.12311/

## 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 (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019)
- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [Grasp Pose Detection in Point Clouds](https://mlsanity.com/paper/1706.09911/) (2017)
- [PointNetGPD: Detecting Grasp Configurations from Point Sets](https://mlsanity.com/paper/1809.06267/) (2018)
- [RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images](https://mlsanity.com/paper/2103.02184/) (2021)
- [A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place](https://mlsanity.com/paper/1509.01277/) (2015)
- [Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking](https://mlsanity.com/paper/1912.12125/) (2019)
- [RGB-D Object Detection and Semantic Segmentation for Autonomous Manipulation in Clutter](https://mlsanity.com/paper/1810.00818/) (2018)
- [Cartman: The low-cost Cartesian Manipulator that won the Amazon Robotics Challenge](https://mlsanity.com/paper/1709.06283/) (2017)
- [Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing](https://mlsanity.com/paper/1810.02977/) (2018)
- [Team Delft's Robot Winner of the Amazon Picking Challenge 2016](https://mlsanity.com/paper/1610.05514/) (2016)
- [Suction Grasp Region Prediction using Self-supervised Learning for Object Picking in Dense Clutter](https://mlsanity.com/paper/1904.07402/) (2019)
- [Estimating 6D Pose From Localizing Designated Surface Keypoints](https://mlsanity.com/paper/1812.01387/) (2018)

## Built on by (most cited first, 30 of 32)

- [Hand-Object Contact Consistency Reasoning for Human Grasps Generation](https://mlsanity.com/paper/2104.03304/) (2021)
- [End-to-End Affordance Learning for Robotic Manipulation](https://mlsanity.com/paper/2209.12941/) (2022)
- [Graspness Discovery in Clutters for Fast and Accurate Grasp Detection](https://mlsanity.com/paper/2406.11142/) (2024)
- [3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification](https://mlsanity.com/paper/2212.00338/) (2022)
- [Challenges for Monocular 6D Object Pose Estimation in Robotics](https://mlsanity.com/paper/2307.12172/) (2023)
- [Dexonomy: Synthesizing All Dexterous Grasp Types in a Grasp Taxonomy](https://mlsanity.com/paper/2504.18829/) (2025)
- [MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis](https://mlsanity.com/paper/2208.03963/) (2022)
- [Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge](https://mlsanity.com/paper/2304.11196/) (2023)
- [Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark](https://mlsanity.com/paper/2305.16378/) (2023)
- [LVDiffusor: Distilling Functional Rearrangement Priors from Large Models into Diffusor](https://mlsanity.com/paper/2312.01474/) (2023)
- [Uncertainty-driven Exploration Strategies for Online Grasp Learning](https://mlsanity.com/paper/2309.12038/) (2023)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [Learning suction graspability considering grasp quality and robot reachability for bin-picking](https://mlsanity.com/paper/2111.02571/) (2021)
- [Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking](https://mlsanity.com/paper/2307.16488/) (2023)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)
- [STOPNet: Multiview-based 6-DoF Suction Detection for Transparent Objects on Production Lines](https://mlsanity.com/paper/2310.05717/) (2023)
- [OptiGrasp: Optimized Grasp Pose Detection Using RGB Images for Warehouse Picking Robots](https://mlsanity.com/paper/2409.19494/) (2024)
- [Corner-Grasp: Multi-Action Grasp Detection and Active Gripper Adaptation for Grasping in Cluttered Environments](https://mlsanity.com/paper/2504.01861/) (2025)
- [Monocular One-Shot Metric-Depth Alignment for RGB-Based Robot Grasping](https://mlsanity.com/paper/2506.17110/) (2025)
- [Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors](https://mlsanity.com/paper/2304.10108/) (2023)
- [Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Picking](https://mlsanity.com/paper/2403.02495/) (2024)
- [Diffusion Suction Grasping with Large-Scale Parcel Dataset](https://mlsanity.com/paper/2502.07238/) (2025)
- [Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction](https://mlsanity.com/paper/2506.09765/) (2025)
- [Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success](https://mlsanity.com/paper/2506.10359/) (2025)
- [MMRNet: Improving Reliability for Multimodal Object Detection and Segmentation for Bin Picking via Multimodal Redundancy](https://mlsanity.com/paper/2210.10842/) (2022)
- [Sim-MEES: Modular End-Effector System Grasping Dataset for Mobile Manipulators in Cluttered Environments](https://mlsanity.com/paper/2305.10580/) (2023)
- [Placeit! A Framework for Learning Robot Object Placement Skills](https://mlsanity.com/paper/2510.09267/) (2025)
- [MultiGraspNet: A Multitask 3D Vision Model for Multi-gripper Robotic Grasping](https://mlsanity.com/paper/2602.06504/) (2026)
- [Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications](https://mlsanity.com/paper/2606.20272/) (2026)
- [Beyond Visual Grasping: Benchmarking Complex Grasping from Detection to Execution](https://mlsanity.com/paper/2607.14341/) (2026)

## 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.