Performing closed-loop grasping at close proximity to an object requires a large field of view. However, such images will inevitably bring large amounts of unnecessary background information, especially when the camera is far away from the target object at the initial stage, resulting in performance degradation of the grasping network. To address this problem, we design a novel PEGG-Net, a real-time, pixel-wise, robotic grasp generation network. The proposed lightweight network is inherently able to learn to remove background noise that can reduce grasping accuracy. Our proposed PEGG-Net achieves improved state-of-the-art performance on both Cornell dataset (98.9%) and Jacquard dataset (93.8%). In the real-world tests, PEGG-Net can support closed-loop grasping at up to 50Hz using an image size of 480x480 in dynamic environments. The trained model also generalizes to previously unseen objects with complex geometrical shapes, household objects and workshop tools and achieved an overall grasp success rate of 91.2% in our real-world grasping experiments.

翻译：近距离执行闭环抓取操作需要大视野图像。然而，这类图像在初始阶段（当相机远离目标物体时）不可避免地会引入大量无关背景信息，导致抓取网络性能下降。为解决该问题，我们设计了一种新型实时逐像素机器人抓取生成网络PEGG-Net。该轻量级网络具备自主去除降低抓取精度的背景噪声的能力。所提出的PEGG-Net在Cornell数据集（98.9%）和Jacquard数据集（93.8%）上均实现了当前最优性能提升。在真实环境测试中，PEGG-Net可在动态环境下以480×480像素图像尺寸支持最高50Hz的闭环抓取。训练模型还能泛化至此前未见过的复杂几何形状物体、家用物品及车间工具，在真实抓取实验中实现了91.2%的综合抓取成功率。