Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks

11 Sep 2020Tom SilverRohan ChitnisAidan CurtisJoshua TenenbaumTomas Lozano-PerezLeslie Pack Kaelbling

Real-world planning problems often involve hundreds or even thousands of objects, straining the limits of modern planners. In this work, we address this challenge by learning to predict a small set of objects that, taken together, would be sufficient for finding a plan... (read more)

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