  Our way of grasping objects is challenging for efficient, intelligent and
optimal grasp by COBOTs. To streamline the process, here we use deep learning
techniques to help robots learn to generate and execute appropriate grasps
quickly. We developed a Generative Inception Neural Network (GI-NNet) model,
capable of generating antipodal robotic grasps on seen as well as unseen
objects. It is trained on Cornell Grasping Dataset (CGD) and attained 98.87%
grasp pose accuracy for detecting both regular and irregular shaped objects
from RGB-Depth (RGB-D) images while requiring only one third of the network
trainable parameters as compared to the existing approaches. However, to attain
this level of performance the model requires the entire 90% of the available
labelled data of CGD keeping only 10% labelled data for testing which makes it
vulnerable to poor generalization. Furthermore, getting sufficient and quality
labelled dataset is becoming increasingly difficult keeping in pace with the
requirement of gigantic networks. To address these issues, we attach our model
as a decoder with a semi-supervised learning based architecture known as Vector
Quantized Variational Auto Encoder (VQVAE), which works efficiently when
trained both with the available labelled and unlabelled data. The proposed
model, which we name as Representation based GI-NNet (RGI-NNet), has been
trained 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 labelled
dataset. For the performance verification of both GI-NNet and RGI-NNet models,
we use Anukul (Baxter) hardware cobot.
