论文标题
运动计划的图形神经网络
Graph Neural Networks for Motion Planning
论文作者
论文摘要
本文研究了使用图神经网络（GNN）解决经典运动计划问题的可行性。我们建议使用GNNS使用称为置换不变性的属性来编码计划空间的拓扑拓扑的能力来指导连续和离散计划算法。我们提出了两种技术，即在密集的固定图上，用于低维问题，以及基于抽样的GNN，以解决高维问题。我们研究了GNN解决计划问题的能力，例如识别关键节点或学习快速探索的随机树（RRT）中的采样分布。临界抽样，摆锤和六个DOF机器人组的实验显示GNNS对传统的分析方法以及使用完全连接或卷积神经网络的学习方法进行了改进。
This paper investigates the feasibility of using Graph Neural Networks (GNNs) for classical motion planning problems. We propose guiding both continuous and discrete planning algorithms using GNNs' ability to robustly encode the topology of the planning space using a property called permutation invariance. We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT). Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
