# FViT-Grasp: Grasping Objects With Using Fast Vision Transformers

arXiv: https://arxiv.org/abs/2311.13986 · HTML: https://mlsanity.com/paper/2311.13986/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours](https://mlsanity.com/paper/1509.06825/) (2015)
- [Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach](https://mlsanity.com/paper/1804.05172/) (2018)
- [Real-Time Grasp Detection Using Convolutional Neural Networks](https://mlsanity.com/paper/1412.3128/) (2014)
- [Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network](https://mlsanity.com/paper/1909.04810/) (2019)
- [Less is More: Pay Less Attention in Vision Transformers](https://mlsanity.com/paper/2105.14217/) (2021)
- [Robotic Grasp Detection using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1611.08036/) (2016)
- [End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGB](https://mlsanity.com/paper/2107.05287/) (2021)
- [ROI-based Robotic Grasp Detection for Object Overlapping Scenes](https://mlsanity.com/paper/1808.10313/) (2018)
- [Fully Convolutional Grasp Detection Network with Oriented Anchor Box](https://mlsanity.com/paper/1803.02209/) (2018)

## Built on by (most cited first, 1 of 1)

- [HMT-Grasp: A Hybrid Mamba-Transformer Approach for Robot Grasping in Cluttered Environments](https://mlsanity.com/paper/2410.03522/) (2024)

## Abstract (arXiv)

This study addresses the challenge of manipulation, a prominent issue in robotics. We have devised a novel methodology for swiftly and precisely identifying the optimal grasp point for a robot to manipulate an object. Our approach leverages a Fast Vision Transformer (FViT), a type of neural network designed for processing visual data and predicting the most suitable grasp location. Demonstrating state-of-the-art performance in terms of speed while maintaining a high level of accuracy, our method holds promise for potential deployment in real-time robotic grasping applications. We believe that this study provides a baseline for future research in vision-based robotic grasp applications. Its high speed and accuracy bring researchers closer to real-life applications.