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Auteurs principaux: Saqib, Danyal, Hussain, Wajahat
Format: Preprint
Publié: 2024
Sujets:
Accès en ligne:https://arxiv.org/abs/2410.14084
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author Saqib, Danyal
Hussain, Wajahat
author_facet Saqib, Danyal
Hussain, Wajahat
contents Learning Based Robot Grasping currently involves the use of labeled data. This approach has two major disadvantages. Firstly, labeling data for grasp points and angles is a strenuous process, so the dataset remains limited. Secondly, human labeling is prone to bias due to semantics. In order to solve these problems we propose a simpler self-supervised robotic setup, that will train a Convolutional Neural Network (CNN). The robot will label and collect the data during the training process. The idea is to make a robot that is less costly, small and easily maintainable in a lab setup. The robot will be trained on a large data set for several hundred hours and then the trained Neural Network can be mapped onto a larger grasping robot.
format Preprint
id arxiv_https___arxiv_org_abs_2410_14084
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Self Supervised Deep Learning for Robot Grasping
Saqib, Danyal
Hussain, Wajahat
Robotics
Computer Vision and Pattern Recognition
Learning Based Robot Grasping currently involves the use of labeled data. This approach has two major disadvantages. Firstly, labeling data for grasp points and angles is a strenuous process, so the dataset remains limited. Secondly, human labeling is prone to bias due to semantics. In order to solve these problems we propose a simpler self-supervised robotic setup, that will train a Convolutional Neural Network (CNN). The robot will label and collect the data during the training process. The idea is to make a robot that is less costly, small and easily maintainable in a lab setup. The robot will be trained on a large data set for several hundred hours and then the trained Neural Network can be mapped onto a larger grasping robot.
title Self Supervised Deep Learning for Robot Grasping
topic Robotics
Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2410.14084