Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its environment. Despite the steady progress in robotic grasping, it is still difficult to achieve both real-time and high accuracy grasping detection. In this paper, we propose a real-time robotic grasp detection method, which can accurately predict potential grasp for parallel-plate robotic grippers using RGB images. Our work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp detector. And for the first time, we add an attention mechanism to the grasp detection task, which enables the network to focus on grasp regions rather than background. Specifically, we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the network. We quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard dataset. Extensive experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art methods. In particular, our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset, giving rise to the accuracy of 98.9% and 95.6%, respectively at real-time calculation speed.

抓取检测是一种视觉识别任务，机器人利用其传感器来检测其环境中的可抓取物体。尽管机器人抓取技术取得了稳步进展，但同时实现实时和高精度的抓取检测仍然很困难。在本文中，我们提出了一种实时机器人抓取检测方法，该方法可以使用 RGB 图像准确预测平行板机器人抓取器的潜在抓取。我们的工作采用端到端卷积神经网络，由特征描述符和抓取检测器组成。我们首次在抓取检测任务中添加了注意力机制，使网络能够专注于抓取区域而不是背景。具体来说，我们在抓取检测方法中提出了角度标签平滑策略，以增强网络的容错能力。我们在公共康奈尔数据集和 Jacquard 数据集上从不同方面定量和定性评估我们的抓取检测方法。大量的实验表明，我们的抓取检测方法取得了优于最先进方法的性能。特别是，我们的抓取检测方法在 Cornell 数据集和 Jacquard 数据集上均排名第一，实时计算速度下的准确率分别为 98.9% 和 95.6%。