mlsanityData to 7 Oct 2026

Deep Learning Approaches to Grasp Synthesis: A Review

arXiv 2207.02556 ↗First posted 6 Jul 2022cs.ROcited by 69

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Abstract (arXiv)

Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep-learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all 6 degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches. Additionally, we found two `supporting methods` around grasping that use deep-learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research. An online version of the survey is available at https://rhys-newbury.github.io/projects/6dof/

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jul 2022.

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Citing papers per year: 2022: 2; 2023: 17; 2024: 16; 2025: 24; 2026: 10.

69 papers.

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  1. 2024Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AIcited by 175
  2. 2025GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Datacited by 120
  3. 2024DexDiffuser: Generating Dexterous Grasps with Diffusion Modelscited by 46
  4. 2025Diffusion Models for Robotic Manipulation: A Surveycited by 39
  5. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 34
  6. 2023Grasp-Anything: Large-scale Grasp Dataset from Foundation Modelscited by 33
  7. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  8. 2025GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Trainingcited by 32
  9. 2023UGG: Unified Generative Graspingcited by 30
  10. 2024SemGrasp: Semantic Grasp Generation via Language Aligned Discretizationcited by 29
  11. 2025RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluationcited by 29
  12. 2024DITTO: Demonstration Imitation by Trajectory Transformationcited by 28
38 more
  1. 2024SUGAR: Pre-training 3D Visual Representations for Roboticscited by 26
  2. 2024Language-driven Grasp Detectioncited by 26
  3. 2023Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Cluttercited by 22
  4. 2023Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Groundingcited by 17
  5. 2024ICGNet: A Unified Approach for Instance-Centric Graspingcited by 16
  6. 2023CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimationcited by 13
  7. 2023GraspLDM: Generative 6-DoF Grasp Synthesis using Latent Diffusion Modelscited by 13
  8. 2025VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibilitycited by 11
  9. 2023SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenescited by 9
  10. 2023Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasetscited by 8
  11. 2025GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenescited by 8
  12. 2023Uncertainty-driven Exploration Strategies for Online Grasp Learningcited by 6
  13. 2023Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Renderingcited by 5
  14. 2023Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Pickingcited by 5
  15. 2023KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D inputcited by 4
  16. 2023Evaluating Continual Learning on a Home Robotcited by 4
  17. 2023ROSGPT_Vision: Commanding Robots Using Only Language Models' Promptscited by 4
  18. 2024Speeding up 6-DoF Grasp Sampling with Quality-Diversitycited by 4
  19. 2024Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3cited by 4
  20. 2025Conditional Latent Diffusion Models for Zero-Shot Instance Segmentationcited by 4
  21. 2025Grasping a Handful: Sequential Multi-Object Dexterous Grasp Generationcited by 3
  22. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challengescited by 3
  23. 2023Natural Language Robot Programming: NLP integrated with autonomous robotic graspingcited by 2
  24. 2023Machine Learning Meets Advanced Robotic Manipulationcited by 2
  25. 2023Toward a Plug-and-Play Vision-Based Grasping Module for Roboticscited by 2
  26. 2024You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objectscited by 2
  27. 2024QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversitycited by 2
  28. 2025Continuous Wrist Control on the Hannes Prosthesis: a Vision-based Shared Autonomy Frameworkcited by 2
  29. 2025GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructionscited by 2
  30. 2025Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridgescited by 2
  31. 2025VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generationcited by 2
  32. 2025GRASPLAT: Enabling dexterous grasping through novel view synthesiscited by 2
  33. 2026Variational Neural Belief Parameterizations for Robust Dexterous Grasping under Multimodal Uncertaintycited by 2
  34. 2022E2R: a Hierarchical-Learning inspired Novelty-Search method to generate diverse repertoires of grasping trajectoriescited by 1
  35. 2024Pseudo-Labeling and Contextual Curriculum Learning for Online Grasp Learning in Robotic Bin Pickingcited by 1
  36. 2025EigenActor: Variant Body-Object Interaction Generation Evolved from Invariant Action Basis Reasoningcited by 1
  37. 2025Simultaneous Pick and Place Detection by Combining SE(3) Diffusion Models with Differential Kinematicscited by 1
  38. 2025Sim2Real Transfer for Vision-Based Grasp Verificationcited by 1

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