# Improving the Successful Robotic Grasp Detection Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2403.05211 · HTML: https://mlsanity.com/paper/2403.05211/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Deep Learning for Detecting Robotic Grasps](https://mlsanity.com/paper/1301.3592/) (2013)
- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [Jacquard: A Large Scale Dataset for Robotic Grasp Detection](https://mlsanity.com/paper/1803.11469/) (2018)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Robotic Grasp Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1611.08036/) (2016)
- [Deep Learning a Grasp Function for Grasping under Gripper Pose Uncertainty](https://mlsanity.com/paper/1608.02239/) (2016)
- [The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research](https://mlsanity.com/paper/1609.05258/) (2016)

## Built on by (most cited first, 3 of 3)

- [Scene Understanding in Pick-and-Place Tasks: Analyzing Transformations Between Initial and Final Scenes](https://mlsanity.com/paper/2409.17720/) (2024)
- [AI-Driven Relocation Tracking in Dynamic Kitchen Environments](https://mlsanity.com/paper/2503.01547/) (2025)
- [Iterative Grasp Pose Refinement: A Deep Reinforcement Learning Approach for 2D Vision](https://mlsanity.com/paper/2608.17628/) (2026)

## Abstract (arXiv)

Robotic grasp should be carried out in a real-time manner by proper accuracy. Perception is the first and significant step in this procedure. This paper proposes an improved pipeline model trying to detect grasp as a rectangle representation for different seen or unseen objects. It helps the robot to start control procedures from nearer to the proper part of the object. The main idea consists in pre-processing, output normalization, and data augmentation to improve accuracy by 4.3 percent without making the system slow. Also, a comparison has been conducted over different pre-trained models like AlexNet, ResNet, Vgg19, which are the most famous feature extractors for image processing in object detection. Although AlexNet has less complexity than other ones, it outperformed them, which helps the real-time property.