# Robotic Grasping from Classical to Modern: A Survey

arXiv: https://arxiv.org/abs/2202.03631 · HTML: https://mlsanity.com/paper/2202.03631/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2022.

## Built on by (most cited first, 14 of 14)

- [ShapeGrasp: Zero-Shot Task-Oriented Grasping with Large Language Models through Geometric Decomposition](https://mlsanity.com/paper/2403.18062/) (2024)
- [Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets](https://mlsanity.com/paper/2310.04517/) (2023)
- [Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations](https://mlsanity.com/paper/2212.02126/) (2022)
- [Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics](https://mlsanity.com/paper/2308.05483/) (2023)
- [3D Whole-body Grasp Synthesis with Directional Controllability](https://mlsanity.com/paper/2408.16770/) (2024)
- [Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT](https://mlsanity.com/paper/2508.08748/) (2025)
- [Speeding up 6-DoF Grasp Sampling with Quality-Diversity](https://mlsanity.com/paper/2403.06173/) (2024)
- [Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies](https://mlsanity.com/paper/2211.10957/) (2022)
- [QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity](https://mlsanity.com/paper/2410.02319/) (2024)
- [GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructions](https://mlsanity.com/paper/2503.16013/) (2025)
- [Grasp2Grasp: Vision-Based Dexterous Grasp Translation via Schr"odinger Bridges](https://mlsanity.com/paper/2506.02489/) (2025)
- [JENGA: Object selection and pose estimation for robotic grasping from a stack](https://mlsanity.com/paper/2506.13425/) (2025)
- [Generative Grasp Detection and Estimation with Concept Learning-based Safety Criteria](https://mlsanity.com/paper/2506.17842/) (2025)
- [Steer2Grasp: Inference-Time Embodiment-Aware Steering for Diverse Physically Feasible Grasp Diffusion](https://mlsanity.com/paper/2609.33546/) (2026)

## Abstract (arXiv)

Robotic Grasping has always been an active topic in robotics since grasping is one of the fundamental but most challenging skills of robots. It demands the coordination of robotic perception, planning, and control for robustness and intelligence. However, current solutions are still far behind humans, especially when confronting unstructured scenarios. In this paper, we survey the advances of robotic grasping, starting from the classical formulations and solutions to the modern ones. By reviewing the history of robotic grasping, we want to provide a complete view of this community, and perhaps inspire the combination and fusion of different ideas, which we think would be helpful to touch and explore the essence of robotic grasping problems. In detail, we firstly give an overview of the analytic methods for robotic grasping. After that, we provide a discussion on the recent state-of-the-art data-driven grasping approaches rising in recent years. With the development of computer vision, semantic grasping is being widely investigated and can be the basis of intelligent manipulation and skill learning for autonomous robotic systems in the future. Therefore, in our survey, we also briefly review the recent progress in this topic. Finally, we discuss the open problems and the future research directions that may be important for the human-level robustness, autonomy, and intelligence of robots.