论文标题
使用pix2pix gan生成质量抓握矩形，以掌握智能机器人
Generating Quality Grasp Rectangle using Pix2Pix GAN for Intelligent Robot Grasping
论文作者
论文摘要
智能机器人抓紧是一项非常具有挑战性的任务，因为它的固有复杂性和无足够标记的数据的可用性。由于在任何基于深度学习的模型（包括深度强化学习）中提供合适的标记数据，可用于有效培训，对于成功的掌握学习至关重要，在本文中，我们建议使用PIX2PIX生成对抗网络（PIX2PIX GAN）来解决生成握把姿势/矩形的问题，该网络（PIX2PIX GAN）将对象作为对象作为输入的图像和盖帽的grestang grestang grectange grectange grectange grectange contecting Rectang rectang rectang rectang rectang rectang。在这里，我们提出了一个端到端的抓矩形生成方法，并将其嵌入到要抓住的对象的适当位置。我们已经开发了两个模块以获得最佳的抓矩形。借助第一个模块，从pix2pix gan的输出中提取了生成的握把矩形的姿势（位置和方向），然后将提取的抓取姿势转化为对象的质心，因为在这里我们假设我们假设像正常对象的人类抓住正常物体的中心一样，是centerable的中心。对于其他不规则形状的对象，我们允许将生成的握把矩形送入机器人以掌握执行。由于我们所提出的方法以87.79％的范围，其精度已显着提高，以产生握把矩形，康奈尔抓地数据集的数量有限。实验表明，我们提出的基于生成模型的方法在执行可见的对象和看不见的对象方面给出了有希望的结果。
Intelligent robot grasping is a very challenging task due to its inherent complexity and non availability of sufficient labelled data. Since making suitable labelled data available for effective training for any deep learning based model including deep reinforcement learning is so crucial for successful grasp learning, in this paper we propose to solve the problem of generating grasping Poses/Rectangles using a Pix2Pix Generative Adversarial Network (Pix2Pix GAN), which takes an image of an object as input and produces the grasping rectangle tagged with the object as output. Here, we have proposed an end-to-end grasping rectangle generating methodology and embedding it to an appropriate place of an object to be grasped. We have developed two modules to obtain an optimal grasping rectangle. With the help of the first module, the pose (position and orientation) of the generated grasping rectangle is extracted from the output of Pix2Pix GAN, and then the extracted grasp pose is translated to the centroid of the object, since here we hypothesize that like the human way of grasping of regular shaped objects, the center of mass/centroids are the best places for stable grasping. For other irregular shaped objects, we allow the generated grasping rectangles as it is to be fed to the robot for grasp execution. The accuracy has significantly improved for generating the grasping rectangle with limited number of Cornell Grasping Dataset augmented by our proposed approach to the extent of 87.79%. Experiments show that our proposed generative model based approach gives the promising results in terms of executing successful grasps for seen as well as unseen objects.
