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Identifying Critical Regions for Motion Planning using Auto-Generated Saliency Labels with Convolutional Neural Networks

by   Daniel Molina, et al.

In this paper, we present a new approach to learning for motion planning (MP) where critical regions of an environment with low probability measure are learned from a given set of motion plans and used to improve performance on new problem instances. We show that a convolutional neural network (CNN) can be used to identify critical regions for motion plans. We also introduce a new sampling-based motion planner, Learn and Link (LLP). LLP leverages critical region locations identified by our CNN to overcome the limitations of uniform sampling, while still maintaining guarantees of correctness inherent to sampling-based algorithms. We evaluate our planner using an extensive suite of experiments on challenging navigation planning problems and compare its performance against planners from the Open Motion Planning Library (OMPL). We show that our approach requires the creation of far fewer states than the existing sampling-based planners.


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