论文标题
学习使用深剩余的U-NET掌握3D对象
Learning to Grasp 3D Objects using Deep Residual U-Nets
论文作者
论文摘要
GRASP合成是任何机器人对象操纵任务的挑战性任务之一。在本文中，我们为3D对象提供了一种新的基于深度学习的掌握合成方法。特别是，我们提出了一个端到端的3D卷积神经网络，以预测对象的可抓地力区域。我们命名了我们的方法RES-U-NET，因为网络的架构是基于U-NET结构和残留网络式块设计的。它设计为为任何所需物体计划6-DOF抓取，有效地计算和使用，并在变化的点云密度和高斯噪声上稳健。我们已经进行了广泛的实验，以评估有关抓手零件检测，掌握成功率以及对变化点云密度和高斯噪声的鲁棒性的拟议方法的性能。实验验证了各个方面所提出的体系结构的有希望的性能。可以在以下网址找到一个视频：http：//youtu.be/5_yajcc8owo在模拟环境中的性能
Grasp synthesis is one of the challenging tasks for any robot object manipulation task. In this paper, we present a new deep learning-based grasp synthesis approach for 3D objects. In particular, we propose an end-to-end 3D Convolutional Neural Network to predict the objects' graspable areas. We named our approach Res-U-Net since the architecture of the network is designed based on U-Net structure and residual network-styled blocks. It devised to plan 6-DOF grasps for any desired object, be efficient to compute and use, and be robust against varying point cloud density and Gaussian noise. We have performed extensive experiments to assess the performance of the proposed approach concerning graspable part detection, grasp success rate, and robustness to varying point cloud density and Gaussian noise. Experiments validate the promising performance of the proposed architecture in all aspects. A video showing the performance of our approach in the simulation environment can be found at: http://youtu.be/5_yAJCc8owo
