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arXiv · 2311.13986

FViT-Grasp: Grasping Objects With Using Fast Vision Transformers

Arda Sarp Yenicesu·Berk Cicek·Ozgur S. Oguz
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Abstract

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.

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Arda Sarp Yenicesu, Berk Cicek, Ozgur S. Oguz. 2023-11-23. FViT-Grasp: Grasping Objects With Using Fast Vision Transformers. https://arxiv.org/abs/2311.13986

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