Self Supervised Deep Learning for Robot Grasping

• URL: http://arxiv.org/abs/2410.14084v1
• Date: Thu, 17 Oct 2024 23:26:55 GMT
• Title: Self Supervised Deep Learning for Robot Grasping
• Authors: Danyal Saqib, Wajahat Hussain,
• Abstract summary: We propose a 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 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.
• Score: 1.1433275758269863
• License:
• Abstract: 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.

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