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Data-Driven Grasp Synthesis - A Survey

arXiv 1309.2660 ↗First posted 10 Sep 2013cs.ROcited by 106

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Abstract (arXiv)

We review the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps. We divide the approaches into three groups based on whether they synthesize grasps for known, familiar or unknown objects. This structure allows us to identify common object representations and perceptual processes that facilitate the employed data-driven grasp synthesis technique. In the case of known objects, we concentrate on the approaches that are based on object recognition and pose estimation. In the case of familiar objects, the techniques use some form of a similarity matching to a set of previously encountered objects. Finally for the approaches dealing with unknown objects, the core part is the extraction of specific features that are indicative of good grasps. Our survey provides an overview of the different methodologies and discusses open problems in the area of robot grasping. We also draw a parallel to the classical approaches that rely on analytic formulations.

Key facts. Cited by 106 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Sep 2013.

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Citing papers per year: 2021: 27; 2022: 17; 2023: 23; 2024: 12; 2025: 16; 2026: 11.

106 papers.

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  1. 2021Neural Fields in Visual Computing and Beyondcited by 540
  2. 2021Habitat 2.0: Training Home Assistants to Rearrange their Habitatcited by 511
  3. 2023Language-Driven Representation Learning for Roboticscited by 165
  4. 2022Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learningcited by 119
  5. 2021Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representationscited by 108
  6. 2021ManipulaTHOR: A Framework for Visual Object Manipulationcited by 102
  7. 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 86
  8. 2022DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulationcited by 84
  9. 2021Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Taskscited by 78
  10. 2022TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinementcited by 70
  11. 2023DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objectscited by 66
  12. 2023NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesiscited by 57
38 more
  1. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  2. 2021End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGBcited by 44
  3. 2025Diffusion Models for Robotic Manipulation: A Surveycited by 41
  4. 2021RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Imagescited by 39
  5. 2023Dynamic Handover: Throw and Catch with Bimanual Handscited by 35
  6. 2023Learning Human-to-Robot Handovers from Point Cloudscited by 34
  7. 2021INVIGORATE: Interactive Visual Grounding and Grasping in Cluttercited by 32
  8. 2021DDGC: Generative Deep Dexterous Grasping in Cluttercited by 29
  9. 2024SemGrasp: Semantic Grasp Generation via Language Aligned Discretizationcited by 29
  10. 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexteritycited by 28
  11. 2023GRIP: Generating Interaction Poses Using Spatial Cues and Latent Consistencycited by 27
  12. 2023MANUS: Markerless Grasp Capture using Articulated 3D Gaussianscited by 26
  13. 2022Learning High-DOF Reaching-and-Grasping via Dynamic Representation of Gripper-Object Interactioncited by 24
  14. 2024GEARS: Local Geometry-aware Hand-object Interaction Synthesiscited by 20
  15. 2023Grasp Multiple Objects with One Handcited by 18
  16. 2025Evaluating Uncertainty and Quality of Vision-Language-Action-enabled Robotscited by 17
  17. 2021Simultaneous Semantic and Collision Learning for 6-DoF Grasp Pose Estimationcited by 14
  18. 2022Contact Points Discovery for Soft-Body Manipulations with Differentiable Physicscited by 14
  19. 2024GenH2R: Learning Generalizable Human-to-Robot Handover via Scalable Simulation, Demonstration, and Imitationcited by 14
  20. 2025Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulationcited by 14
  21. 2023SynH2R: Synthesizing Hand-Object Motions for Learning Human-to-Robot Handoverscited by 13
  22. 2023CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimationcited by 13
  23. 2023GraspLDM: Generative 6-DoF Grasp Synthesis using Latent Diffusion Modelscited by 13
  24. 2022Visuo-Haptic Object Perception for Robots: An Overviewcited by 12
  25. 2022NeuralGrasps: Learning Implicit Representations for Grasps of Multiple Robotic Handscited by 12
  26. 2021Collision-Aware Target-Driven Object Grasping in Constrained Environmentscited by 11
  27. 2021REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Cluttercited by 11
  28. 2021Learning to Regrasp by Learning to Placecited by 11
  29. 2021Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domainscited by 10
  30. 2021OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimationcited by 9
  31. 2022Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Searchcited by 9
  32. 2024ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Cameracited by 9
  33. 2024PRIME: Scaffolding Manipulation Tasks with Behavior Primitives for Data-Efficient Imitation Learningcited by 8
  34. 2025Attribute-Based Robotic Grasping with Data-Efficient Adaptationcited by 8
  35. 2021Attribute-Based Robotic Grasping with One-Grasp Adaptationcited by 7
  36. 2021Transferring Experience from Simulation to the Real World for Precise Pick-And-Place Tasks in Highly Cluttered Scenescited by 6
  37. 2021A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitivescited by 6
  38. 2023Uncertainty-driven Exploration Strategies for Online Grasp Learningcited by 6

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