2020/06/11 by Arbaaz Khan, Alejandro Ribeiro, Khan, Arbaaz +6 · 2 citations
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Human Pose and Action Recognition #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Robotic Path Planning Algorithms #Robotics (cs.RO) #cs.AI #cs.LG #cs.RO
paper · pdf · doi:10.48550/arxiv.2006.06248
openalex publication_date 2020/06/11 · arxiv created 2020/12/14 · arxiv updated 2020/12/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.