Vision Transformer and Residual Network-Based Autoencoder for RGBD Data Processing in Robotic Grasping of Noodle-Like Objects

In this innovative study, a Vision Transformer and Residual Network-based Autoencoder is employed for the efficient encoding of RGBD data, aimed at enhancing robotic precision in grasping noodle-like objects. The project successfully compresses 50x50 pixel RGBD images to a 1024-element format, optim...

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Bibliographic Details

Published in:	2024 1st International Conference on Robotics, Engineering, Science, and Technology (RESTCON) pp. 85 - 89
Main Authors:	Koomklang, Nattapat, Gamolped, Prem, Hayashi, Eiji
Format:	Conference Proceeding
Language:	English
Published:	IEEE 16.02.2024
Subjects:	Autoencoder Data processing Deep Learning Grasping Image coding Measurement Residual Network RGBD Robot Grasping Service robots Transformers Transforms Vision Transformer
Online Access:	Get full text
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