Optimal Solving of Constrained PathPlanning Problems with Graph Convolutional Networks and Optimized Tree Search Learningbased methods are growing prominence for planning purposes. However, there are very few approaches for learningassisted constrained pathplanning on graphs, while there are multiple downstream practical applications. This is the case for constrained pathplanning for Autonomous Unmanned Ground Vehicles (AUGV), typically deployed in disaster relief or search and rescue applications. In offroad environments, the AUGV must dynamically optimize a sourcedestination path under various operational constraints, out of which several are difficult to predict in advance and need to be addressed online. We propose a hybrid solving planner that combines machine learning models and an optimal solver. More specifically, a graph convolutional network(GCN) is used to assist a branch and bound(Bu0026B) algorithm in handling the constraints. We conduct experiments on realistic scenarios and show that GCN support enables substantial speedup and smoother scaling to harder problems.

LEGO: Leveraging Experience in Roadmap Generation for SamplingBased Planning We consider the problem of leveraging prior experience to generate roadmaps in samplingbased motion planning. A desirable roadmap is one that is sparse, allowing for fast search, with nodes spread out at key locations such that a low cost feasible path exists. An increasingly popular approach is to learn a distribution of nodes that would produce such a roadmap. Stateoftheart is to train a conditional variational autoencoder (CVAE) on the prior dataset with the shortest paths as target input. While this is quite effective on many problems, we show it can fail in the face of complex obstacle configurations or mismatch between training and testing. We present an algorithm LEGO that addresses these issues by training the CVAE with target samples that satisfy two important criteria. Firstly, these samples belong only to bottleneck regions along nearoptimal paths that are otherwise difficult tosample with a uniform sampler. Secondly, these samples are spread out across diverse regions to maximize the likelihood of a feasible path existing. We formally define these properties and prove performance guarantees for LEGO. We extensively evaluate LEGO on a range of planning problems, including robot arm planning, and report significant gains over heuristics as well as learned baselines.