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)

PDF Abstract


No code implementations yet. Submit your code now

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet