# SGDN: Segmentation-Based Grasp Detection Network For Unsymmetrical Three-Finger Gripper

arXiv: https://arxiv.org/abs/2005.08222 · HTML: https://mlsanity.com/paper/2005.08222/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2020.

## Built on by (most cited first, 2 of 2)

- [NBMOD: Find It and Grasp It in Noisy Background](https://mlsanity.com/paper/2306.10265/) (2023)
- [Lightweight Convolutional Neural Network with Gaussian-based Grasping Representation for Robotic Grasping Detection](https://mlsanity.com/paper/2101.10226/) (2021)

## Abstract (arXiv)

In this paper, we present Segmentation-Based Grasp Detection Network (SGDN) to predict a feasible robotic grasping for a unsymmetrical three-finger robotic gripper using RGB images. The feasible grasping of a target should be a collection of grasp regions with the same grasp angle and width. In other words, a simplified planar grasp representation should be pixel-level rather than region-level such as five-dimensional grasp representation.Therefore, we propose a pixel-level grasp representation, oriented base-fixed triangle. It is also more suitable for unsymmetrical three-finger gripper which cannot grasp symmetrically when grasping some objects, the grasp angle is at [0, 2π) instead of [0, π) of parallel plate gripper.In order to predict the appropriate grasp region and its corresponding grasp angle and width in the RGB image, SGDN uses DeepLabv3+ as a feature extractor, and uses a three-channel grasp predictor to predict feasible oriented base-fixed triangle grasp representation of each pixel.On the re-annotated Cornell Grasp Dataset, our model achieves an accuracy of 96.8% and 92.27% on image-wise split and object-wise split respectively, and obtains accurate predictions consistent with the state-of-the-art methods.