  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.
