In the modern era of Deep Learning, network parameters play a vital role in
models efficiency but it has its own limitations like extensive computations
and memory requirements, which may not be suitable for real time intelligent
robot grasping tasks. Current research focuses on how the model efficiency can
be maintained by introducing sparsity but without compromising accuracy of the
model in the robot grasping domain. More specifically, in this research two
light-weighted neural networks have been introduced, namely Sparse-GRConvNet
and Sparse-GINNet, which leverage sparsity in the robotic grasping domain for
grasp pose generation by integrating the Edge-PopUp algorithm. This algorithm
facilitates the identification of the top K% of edges by considering their
respective score values. Both the Sparse-GRConvNet and Sparse-GINNet models are
designed to generate high-quality grasp poses in real-time at every pixel
location, enabling robots to effectively manipulate unfamiliar objects. We
extensively trained our models using two benchmark datasets: Cornell Grasping
Dataset (CGD) and Jacquard Grasping Dataset (JGD). Both Sparse-GRConvNet and
Sparse-GINNet models outperform the current state-of-the-art methods in terms
of performance, achieving an impressive accuracy of 97.75% with only 10% of the
weight of GR-ConvNet and 50% of the weight of GI-NNet, respectively, on CGD.
Additionally, Sparse-GRConvNet achieve an accuracy of 85.77% with 30% of the
weight of GR-ConvNet and Sparse-GINNet achieve an accuracy of 81.11% with 10%
of the weight of GI-NNet on JGD. To validate the performance of our proposed
models, we conducted extensive experiments using the Anukul (Baxter) hardware
cobot