# Classification based Grasp Detection using Spatial Transformer Network

arXiv: https://arxiv.org/abs/1803.01356 · HTML: https://mlsanity.com/paper/1803.01356/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent](https://mlsanity.com/paper/2308.01552/) (2023)
- [Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection](https://mlsanity.com/paper/2101.10226/) (2021)
- [Data-efficient learning of object-centric grasp preferences](https://mlsanity.com/paper/2203.00384/) (2022)
- [Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging](https://mlsanity.com/paper/2109.01880/) (2021)
- [MontageGAN: Generation and Assembly of Multiple Components by GANs](https://mlsanity.com/paper/2205.15577/) (2022)
- [Benchmark Analysis of Various Pre-trained Deep Learning Models on ASSIRA Cats and Dogs Dataset](https://mlsanity.com/paper/2401.04666/) (2024)

## Abstract (arXiv)

Robotic grasp detection task is still challenging, particularly for novel objects. With the recent advance of deep learning, there have been several works on detecting robotic grasp using neural networks. Typically, regression based grasp detection methods have outperformed classification based detection methods in computation complexity with excellent accuracy. However, classification based robotic grasp detection still seems to have merits such as intermediate step observability and straightforward back propagation routine for end-to-end training. In this work, we propose a novel classification based robotic grasp detection method with multiple-stage spatial transformer networks (STN). Our proposed method was able to achieve state-of-the-art performance in accuracy with real- time computation. Additionally, unlike other regression based grasp detection methods, our proposed method allows partial observation for intermediate results such as grasp location and orientation for a number of grasp configuration candidates.