# Deep Learning Approaches to Grasp Synthesis: A Review

arXiv: https://arxiv.org/abs/2207.02556 · HTML: https://mlsanity.com/paper/2207.02556/

## Key facts

- Cited by 72 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2022.

## Built on by (most cited first, 30 of 72)

- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data](https://mlsanity.com/paper/2505.03233/) (2025)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [The Essential Role of Causality in Foundation World Models for Embodied AI](https://mlsanity.com/paper/2402.06665/) (2024)
- [RoboEval: Where Robotic Manipulation Meets Structured and Scalable Evaluation](https://mlsanity.com/paper/2507.00435/) (2025)
- [GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training](https://mlsanity.com/paper/2507.13097/) (2025)
- [UGG: Unified Generative Grasping](https://mlsanity.com/paper/2311.16917/) (2023)
- [DITTO: Demonstration Imitation by Trajectory Transformation](https://mlsanity.com/paper/2403.15203/) (2024)
- [SemGrasp: Semantic Grasp Generation via Language Aligned Discretization](https://mlsanity.com/paper/2404.03590/) (2024)
- [SUGAR: Pre-training 3D Visual Representations for Robotics](https://mlsanity.com/paper/2404.01491/) (2024)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter](https://mlsanity.com/paper/2311.05779/) (2023)
- [Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding](https://mlsanity.com/paper/2301.11564/) (2023)
- [ICGNet: A Unified Approach for Instance-Centric Grasping](https://mlsanity.com/paper/2401.09939/) (2024)
- [CenterGrasp: Object-Aware Implicit Representation Learning for Simultaneous Shape Reconstruction and 6-DoF Grasp Estimation](https://mlsanity.com/paper/2312.08240/) (2023)
- [GraspLDM: Generative 6-DoF Grasp Synthesis using Latent Diffusion Models](https://mlsanity.com/paper/2312.11243/) (2023)
- [VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibility](https://mlsanity.com/paper/2503.12609/) (2025)
- [SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes](https://mlsanity.com/paper/2306.15620/) (2023)
- [Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets](https://mlsanity.com/paper/2310.04517/) (2023)
- [GraspClutter6D: A Large-scale Real-world Dataset for Robust Perception and Grasping in Cluttered Scenes](https://mlsanity.com/paper/2504.06866/) (2025)
- [Uncertainty-driven Exploration Strategies for Online Grasp Learning](https://mlsanity.com/paper/2309.12038/) (2023)
- [Evaluating Continual Learning on a Home Robot](https://mlsanity.com/paper/2306.02413/) (2023)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [Model-free Grasping with Multi-Suction Cup Grippers for Robotic Bin Picking](https://mlsanity.com/paper/2307.16488/) (2023)
- [KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input](https://mlsanity.com/paper/2303.05617/) (2023)
- [ROSGPT_Vision: Commanding Robots Using Only Language Models' Prompts](https://mlsanity.com/paper/2308.11236/) (2023)
- [Speeding up 6-DoF Grasp Sampling with Quality-Diversity](https://mlsanity.com/paper/2403.06173/) (2024)

## 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/