论文标题
一种用于机器人掌握高斯指导检测的新型生成卷积神经网络
A Novel Generative Convolutional Neural Network for Robot Grasp Detection on Gaussian Guidance
论文作者
论文摘要
基于视觉的GRASP检测方法是机器人技术领域的重要研究方向。然而，由于grasp检测矩形的限制的矩形度量，会发生假阳性抓握，从而导致现实世界机器人掌握的任务失败。在本文中，我们提出了一种新型的生成卷积神经网络模型，以提高机器人在现实世界中的抓地力检测的准确性和鲁棒性。首先，使用一种基于高斯的指导训练方法来编码抓握点的质量和抓握姿势的抓握角度，突出了最高质量的抓点位置和抓地角，并降低了假阳性抓地力的产生。同时，使用可变形的卷积来获得对象的形状特征，以指导后续网络到达位置。此外，引入了一种全局本地特征融合方法，以便在功能重建阶段有效地获得更精细的功能，从而使网络专注于握把对象的特征。在Cornell抓握数据集和Jacquard数据集上，我们的方法分别实现了99.0 $ \％$和95.9 $ \％$的出色性能。最后，在现实世界的机器人抓住方案中，提出的方法进行了测试。
The vision-based grasp detection method is an important research direction in the field of robotics. However, due to the rectangle metric of the grasp detection rectangle's limitation, a false-positive grasp occurs, resulting in the failure of the real-world robot grasp task. In this paper, we propose a novel generative convolutional neural network model to improve the accuracy and robustness of robot grasp detection in real-world scenes. First, a Gaussian-based guided training method is used to encode the quality of the grasp point and grasp angle in the grasp pose, highlighting the highest-quality grasp point position and grasp angle and reducing the generation of false-positive grasps. Simultaneously, deformable convolution is used to obtain the shape features of the object in order to guide the subsequent network to the position. Furthermore, a global-local feature fusion method is introduced in order to efficiently obtain finer features during the feature reconstruction stage, allowing the network to focus on the features of the grasped objects. On the Cornell Grasping Datasets and Jacquard Datasets, our method achieves excellent performance of 99.0$\%$ and 95.9$\%$, respectively. Finally, the proposed method is put to the test in a real-world robot grasping scenario.
