在这节课中，我们将讨论GNN的可迁移性，也就是说能够在保证性能的情况下迁移机器学习模型。首先，我们深入研究了谱域和节点域的graphon滤波器的收敛性。稍后，我们将以生成模型的形式讨论graphon过滤器。我们将继续介绍graphon 神经网络(WNNs)，这是解释为什么graphon 神经网络可以在从graphon 获得的确定性图之间转换的关键元素。我们最后证明GNN继承了图滤波器的可迁移性。
This paper proposes RUR53: an Unmanned Ground Vehicle able to autonomously navigate through, identify, and reach areas of interest; and there recognize, localize, and manipulate work tools to perform complex manipulation tasks. The proposed contribution includes a modular software architecture where each module solves specific sub-tasks and that can be easily enlarged to satisfy new requirements. Included indoor and outdoor tests demonstrate the capability of the proposed system to autonomously detect a target object (a panel) and precisely dock in front of it while avoiding obstacles. They show it can autonomously recognize and manipulate target work tools (i.e., wrenches and valve stems) to accomplish complex tasks (i.e., use a wrench to rotate a valve stem). A specific case study is described where the proposed modular architecture lets easy switch to a semi-teleoperated mode. The paper exhaustively describes description of both the hardware and software setup of RUR53, its performance when tests at the 2017 Mohamed Bin Zayed International Robotics Challenge, and the lessons we learned when participating at this competition, where we ranked third in the Gran Challenge in collaboration with the Czech Technical University in Prague, the University of Pennsylvania, and the University of Lincoln (UK).