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.
@article{arxiv.2311.13986,
title = {FViT-Grasp: Grasping Objects With Using Fast Vision Transformers},
author = {Arda Sarp Yenicesu and Berk Cicek and Ozgur S. Oguz},
journal= {arXiv preprint arXiv:2311.13986},
year = {2023}
}