グラフニューラルネットワークによる運動計画
Graph Neural Networks for Motion Planning
計画空間のトポロジーを順列不変性で符号化するGNNを用い、連続・離散の運動計画アルゴリズムを誘導する手法を提案し、RRTのサンプリング分布学習などで従来法を上回ることを示した。
著者: Arbaaz Khan, Alejandro Ribeiro, Vijay Kumar, Anthony G. Francis
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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.
