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.

翻译：在深度学习时代，网络参数对模型效率具有至关重要的影响，但大量参数带来的庞大计算量与内存需求成为其固有局限，难以满足智能机器人实时抓取任务的需求。当前研究聚焦于如何在保持机器人抓取领域模型精度的前提下，通过引入稀疏性来维持模型效率。具体而言，本研究提出了两种轻量级神经网络——Sparse-GRConvNet和Sparse-GINNet，通过整合Edge-PopUp算法在机器人抓取领域利用稀疏性生成抓取姿态。该算法通过评估边的分数值筛选出排名前K%的有效连接。Sparse-GRConvNet和Sparse-GINNet模型均能实时在每个像素位置生成高质量抓取姿态，使机器人能够有效操作陌生物体。我们使用Cornell抓取数据集（CGD）和Jacquard抓取数据集（JGD）这两个基准数据集对模型进行了充分训练。实验结果表明，Sparse-GRConvNet和Sparse-GINNet模型在性能上均超越当前最先进方法：在CGD数据集上，Sparse-GRConvNet仅需GR-ConvNet 10%的参数量即可达到97.75%的准确率，Sparse-GINNet仅需GI-NNet 50%的参数量即可达到同等精度；在JGD数据集上，Sparse-GRConvNet以GR-ConvNet 30%的参数量实现85.77%的准确率，Sparse-GINNet以GI-NNet 10%的参数量实现81.11%的准确率。为验证所提模型性能，我们使用Anukul（Baxter）协作机器人硬件平台开展了大量实验。