Grasping objects intelligently is a challenging task even for humans and we spend a considerable amount of time during our childhood to learn how to grasp objects correctly. In the case of robots, we can not afford to spend that much time on making it to learn how to grasp objects effectively. Therefore, in the present research we propose an efficient learning architecture based on VQVAE so that robots can be taught with sufficient data corresponding to correct grasping. However, getting sufficient labelled data is extremely difficult in the robot grasping domain. To help solve this problem, a semi-supervised learning based model which has much more generalization capability even with limited labelled data set, has been investigated. Its performance shows 6\% improvement when compared with existing state-of-the-art models including our earlier model. During experimentation, It has been observed that our proposed model, RGGCNN2, performs significantly better, both in grasping isolated objects as well as objects in a cluttered environment, compared to the existing approaches which do not use unlabelled data for generating grasping rectangles. To the best of our knowledge, developing an intelligent robot grasping model (based on semi-supervised learning) trained through representation learning and exploiting the high-quality learning ability of GGCNN2 architecture with the limited number of labelled dataset together with the learned latent embeddings, can be used as a de-facto training method which has been established and also validated in this paper through rigorous hardware experimentations using Baxter (Anukul) research robot.

翻译：聪明地研磨物体是即使对人类来说也是一项挑战性的任务,而且我们在童年时期花费了相当长的时间来学习如何正确掌握物体。对于机器人来说,我们不能花这么长的时间来学习如何有效地掌握物体。因此,在目前的研究中,我们提议了一个基于VQVAE的高效学习架构,以便机器人能够掌握足够的数据,从而在掌握正确掌握数据方面掌握足够的数据。然而,在机器人掌握的领域,获得足够的贴标签数据是极其困难的。为了帮助解决这一问题,已经调查了一个半监督的学习基础模型,该模型即使有有限的标签数据集,也具有更广泛的能力。它的性能表明,与现有最先进的模型相比,我们无法花这么多的时间来学习如何有效掌握物体。在试验期间,我们提出的模型,即RGGGCNN2,在掌握孤立的物体以及被污染环境中的物体方面表现要好得多,而现有的方法并不使用不贴标签的数据来掌握矩。为了最佳的知识,我们开发了一个智能的机器人模型,比现有的最高级的机精度模型, 也利用了精度学习高的G级的机能学习方法。