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DDGC: Generative Deep Dexterous Grasping in Clutter

arXiv 2103.04783 ↗First posted 8 Mar 2021cs.RO cs.LGcited by 28

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Abstract (arXiv)

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-Of-Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the other hand, remains mostly unexplored due to the added difficulty of reasoning over obstacles which greatly increases the computational time to generate high-quality collision-free grasps. In this work we address such limitations by introducing DDGC, a fast generative multi-finger grasp sampling method that can generate high quality grasps in cluttered scenes from a single RGB-D image. DDGC is built as a network that encodes scene information to produce coarse-to-fine collision-free grasp poses and configurations. We experimentally benchmark DDGC against the simulated-annealing planner in GraspIt! on 1200 simulated cluttered scenes and 7 real world scenes. The results show that DDGC outperforms the baseline on synthesizing high-quality grasps and removing clutter while being 5 times faster. This, in turn, opens up the door for using multi-finger grasps in practical applications which has so far been limited due to the excessive computation time needed by other methods.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 8 Mar 2021.

Builds on

19 of 42 references.

References · 19

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Improved Training of Wasserstein GANscited by 2,223
  3. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 312
  4. 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 259
  5. 20196-DOF GraspNet: Variational Grasp Generation for Object Manipulationcited by 237
  6. 2018Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approachcited by 117
  7. 2017Grasp Pose Detection in Point Cloudscited by 107
  8. 2013Data-Driven Grasp Synthesis - A Surveycited by 104
  9. 2016Shape Completion Enabled Robotic Graspingcited by 79
  10. 20196-DOF Grasping for Target-driven Object Manipulation in Cluttercited by 70
  11. 2020EGAD! an Evolved Grasping Analysis Dataset for diversity and reproducibility in robotic manipulationcited by 61
  12. 2016High precision grasp pose detection in dense cluttercited by 33
7 more
  1. 2020Multi-FinGAN: Generative Coarse-To-Fine Sampling of Multi-Finger Graspscited by 20
  2. 2019Robust Grasp Planning Over Uncertain Shape Completionscited by 16
  3. 2018Multi-Modal Geometric Learning for Grasping and Manipulationcited by 15
  4. 2020Amodal 3D Reconstruction for Robotic Manipulation via Stability and Connectivitycited by 15
  5. 2020Multi-Fingered Active Grasp Learningcited by 13
  6. 2019Beyond Top-Grasps Through Scene Completioncited by 8
  7. 2019Deep Dexterous Grasping of Novel Objects from a Single Viewcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 4; 2023: 5; 2024: 7; 2025: 8; 2026: 2.

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  1. 2022DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulationcited by 129
  2. 2025DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Graspingcited by 87
  3. 2022GenDexGrasp: Generalizable Dexterous Graspingcited by 54
  4. 2024DexDiffuser: Generating Dexterous Grasps with Diffusion Modelscited by 46
  5. 2024DexGraspNet 2.0: Learning Generative Dexterous Grasping in Large-scale Synthetic Cluttered Scenescited by 41
  6. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 34
  7. 2024RealDex: Towards Human-like Grasping for Robotic Dexterous Handcited by 33
  8. 2023UGG: Unified Generative Graspingcited by 30
  9. 2025DexGrasp Anything: Towards Universal Robotic Dexterous Grasping with Physics Awarenesscited by 25
  10. 2025Dex1B: Learning with 1B Demonstrations for Dexterous Manipulationcited by 23
  11. 2023Grasp Multiple Objects with One Handcited by 18
  12. 2025AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiencycited by 14
16 more
  1. 2022Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained Object Detectioncited by 8
  2. 2024ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mappingcited by 8
  3. 2025GAGrasp: Geometric Algebra Diffusion for Dexterous Graspingcited by 7
  4. 2025EvolvingGrasp: Evolutionary Grasp Generation via Efficient Preference Alignmentcited by 6
  5. 2021Simulation-based Bayesian inference for multi-fingered robotic graspingcited by 5
  6. 2023Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Renderingcited by 5
  7. 2024Single-View Scene Point Cloud Human Grasp Generationcited by 4
  8. 2021V-MAO: Generative Modeling for Multi-Arm Manipulation of Articulated Objectscited by 3
  9. 2025Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generationcited by 3
  10. 2024Bimanual Grasp Synthesis for Dexterous Robot Handscited by 2
  11. 2025Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridgescited by 2
  12. 2022Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touchcited by 1
  13. 2023CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Networkcited by 1
  14. 2023DMFC-GraspNet: Differentiable Multi-Fingered Robotic Grasp Generation in Cluttered Scenescited by 1
  15. 2026AutoDex: An Automated Real-World System for Dexterous Grasping Data Collectioncited by 1
  16. 2026BLaDA: Bridging Language to Functional Dexterous Actions within 3DGS Fieldscited by 0