# Grasp Learning: Models, Methods, and Performance

arXiv: https://arxiv.org/abs/2211.04895 · HTML: https://mlsanity.com/paper/2211.04895/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2022.

## Built on by (most cited first, 8 of 8)

- [Grasp-Anything: Large-scale Grasp Dataset from Foundation Models](https://mlsanity.com/paper/2309.09818/) (2023)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [On Robot Grasp Learning Using Equivariant Models](https://mlsanity.com/paper/2306.06489/) (2023)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [Information-driven Affordance Discovery for Efficient Robotic Manipulation](https://mlsanity.com/paper/2405.03865/) (2024)
- [Grasp Diffusion Network: Learning Grasp Generators from Partial Point Clouds with Diffusion Models in SO(3)xR3](https://mlsanity.com/paper/2412.08398/) (2024)
- [Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces](https://mlsanity.com/paper/2308.07807/) (2023)
- [Language-driven Grasp Detection with Mask-guided Attention](https://mlsanity.com/paper/2407.19877/) (2024)

## Abstract (arXiv)

Grasp learning has become an exciting and important topic in robotics. Just a few years ago, the problem of grasping novel objects from unstructured piles of clutter was considered a serious research challenge. Now, it is a capability that is quickly becoming incorporated into industrial supply chain automation. How did that happen? What is the current state of the art in robotic grasp learning, what are the different methodological approaches, and what machine learning models are used? This review attempts to give an overview of the current state of the art of grasp learning research.