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Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics

arXiv 1703.09312 ↗First posted 27 Mar 2017cs.ROcited by 259

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Abstract (arXiv)

To reduce data collection time for deep learning of robust robotic grasp plans, we explore training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp metrics generated from thousands of 3D models from Dex-Net 1.0 in randomized poses on a table. We use the resulting dataset, Dex-Net 2.0, to train a Grasp Quality Convolutional Neural Network (GQ-CNN) model that rapidly predicts the probability of success of grasps from depth images, where grasps are specified as the planar position, angle, and depth of a gripper relative to an RGB-D sensor. Experiments with over 1,000 trials on an ABB YuMi comparing grasp planning methods on singulated objects suggest that a GQ-CNN trained with only synthetic data from Dex-Net 2.0 can be used to plan grasps in 0.8sec with a success rate of 93% on eight known objects with adversarial geometry and is 3x faster than registering point clouds to a precomputed dataset of objects and indexing grasps. The Dex-Net 2.0 grasp planner also has the highest success rate on a dataset of 10 novel rigid objects and achieves 99% precision (one false positive out of 69 grasps classified as robust) on a dataset of 40 novel household objects, some of which are articulated or deformable. Code, datasets, videos, and supplementary material are available at http://berkeleyautomation.github.io/dex-net .

Key facts. Cited by 259 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Mar 2017.

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Citing papers per year: 2021: 57; 2022: 47; 2023: 50; 2024: 42; 2025: 38; 2026: 25.

259 papers.

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  1. 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Controlcited by 2,993
  2. 2025π0.5: a Vision-Language-Action Model with Open-World Generalizationcited by 1,653
  3. 2022BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learningcited by 517
  4. 2024Evaluating Real-World Robot Manipulation Policies in Simulationcited by 484
  5. 2021Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenescited by 213
  6. 2021ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrationscited by 193
  7. 2023Language-Driven Representation Learning for Roboticscited by 163
  8. 2022Human-to-Robot Imitation in the Wildcited by 157
  9. 2022NeRF: Neural Radiance Field in 3D Vision: A Comprehensive Review (Updated Post-Gaussian Splatting)cited by 156
  10. 2021A System for General In-Hand Object Re-Orientationcited by 155
  11. 2023Open-World Object Manipulation using Pre-trained Vision-Language Modelscited by 155
  12. 2022Ditto: Building Digital Twins of Articulated Objects from Interactioncited by 152
38 more
  1. 2024Data Scaling Laws in Imitation Learning for Robotic Manipulationcited by 144
  2. 2021ContactOpt: Optimizing Contact to Improve Graspscited by 137
  3. 2023RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketchescited by 131
  4. 2023Distilled Feature Fields Enable Few-Shot Language-Guided Manipulationcited by 130
  5. 2024MOKA: Open-World Robotic Manipulation through Mark-Based Visual Promptingcited by 126
  6. 2022SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusioncited by 125
  7. 2025GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Datacited by 120
  8. 2021Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objectscited by 119
  9. 2024SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learningcited by 113
  10. 2021Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representationscited by 107
  11. 2022Navigating to Objects in the Real Worldcited by 104
  12. 2023HomeRobot: Open-Vocabulary Mobile Manipulationcited by 104
  13. 2023On Bringing Robots Homecited by 101
  14. 2021gradSim: Differentiable simulation for system identification and visuomotor controlcited by 100
  15. 2022DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Videocited by 83
  16. 2022You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstrationcited by 79
  17. 2021VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objectscited by 78
  18. 2022DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulationcited by 78
  19. 2024GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Graspingcited by 77
  20. 2021Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Taskscited by 75
  21. 2021Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapescited by 70
  22. 2023Multi-Stage Cable Routing through Hierarchical Imitation Learningcited by 59
  23. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  24. 2023NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesiscited by 56
  25. 2021Human Hands as Probes for Interactive Object Understandingcited by 51
  26. 2022HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarioscited by 51
  27. 2024Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstructioncited by 51
  28. 2021CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulationcited by 49
  29. 2024Graspness Discovery in Clutters for Fast and Accurate Grasp Detectioncited by 47
  30. 2023Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictionscited by 46
  31. 2024Robot Utility Models: General Policies for Zero-Shot Deployment in New Environmentscited by 43
  32. 2022IFOR: Iterative Flow Minimization for Robotic Object Rearrangementcited by 42
  33. 2021Sample-Efficient Safety Assurances using Conformal Predictioncited by 41
  34. 2024GraspSplats: Efficient Manipulation with 3D Feature Splattingcited by 41
  35. 2023M2T2: Multi-Task Masked Transformer for Object-centric Pick and Placecited by 40
  36. 2024RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulationcited by 40
  37. 2021Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objectscited by 39
  38. 2023KITE: Keypoint-Conditioned Policies for Semantic Manipulationcited by 39

50 most cited of 259 All 259 (JSON)