FViT-Grasp: Grasping Objects With Using Fast Vision Transformers
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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.
Key facts. Cited by 1 paper 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
11 of 19 references.
References · 11
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 209
- 2018Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approachcited by 120
- 2014Real-Time Grasp Detection Using Convolutional Neural Networkscited by 76
- 2019Antipodal Robotic Grasping using Generative Residual Convolutional Neural Networkcited by 59
- 2021Less is More: Pay Less Attention in Vision Transformerscited by 53
- 2016Robotic Grasp Detection using Deep Convolutional Neural Networkscited by 47
- 2021End-to-end Trainable Deep Neural Network for Robotic Grasp Detection and Semantic Segmentation from RGBcited by 44
- 2018ROI-based Robotic Grasp Detection for Object Overlapping Scenescited by 31
- 2018Fully Convolutional Grasp Detection Network with Oriented Anchor Boxcited by 27
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1 paper.