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Our way of grasping objects is challenging for efficient, intelligent andoptimal grasp by COBOTs. To streamline the process, here we use deep learningtechniques to help robots learn to generate and execute appropriate graspsquickly. We developed a Generative Inception Neural Network (GI-NNet) model,capable of generating antipodal robotic grasps on seen as well as unseenobjects. It is trained on Cornell Grasping Dataset (CGD) and attained 98.87%grasp pose accuracy for detecting both regular and irregular shaped objectsfrom RGB-Depth (RGB-D) images while requiring only one third of the networktrainable parameters as compared to the existing approaches. However, to attainthis level of performance the model requires the entire 90% of the availablelabelled data of CGD keeping only 10% labelled data for testing which makes itvulnerable to poor generalization. Furthermore, getting sufficient and qualitylabelled dataset is becoming increasingly difficult keeping in pace with therequirement of gigantic networks. To address these issues, we attach our modelas a decoder with a semi-supervised learning based architecture known as VectorQuantized Variational Auto Encoder (VQVAE), which works efficiently whentrained both with the available labelled and unlabelled data. The proposedmodel, which we name as Representation based GI-NNet (RGI-NNet), has beentrained with various splits of label data on CGD with as minimum as 10%labelled dataset together with latent embedding generated from VQVAE up to 50%labelled data with latent embedding obtained from VQVAE. The performance level,in terms of grasp pose accuracy of RGI-NNet, varies between 92.13% to 95.6%which is far better than several existing models trained with only labelleddataset. For the performance verification of both GI-NNet and RGI-NNet models,we use Anukul (Baxter) hardware cobot.

GI-NNet \& RGI-NNet: Development of Robotic Grasp Pose Models, Trainable  with Large as well as Limited Labelled Training Datasets, under supervised  and semi supervised paradigms