  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. We propose a graph neural network
architecture for predicting object importance in a single inference pass, thus
incurring little overhead while greatly reducing the number of objects that
must be considered by the planner. Our approach treats the planner and
transition model as black boxes, and can be used with any off-the-shelf
planner. Empirically, across classical planning, probabilistic planning, and
robotic task and motion planning, we find that our method results in planning
that is significantly faster than several baselines, including other partial
grounding strategies and lifted planners. We conclude that learning to predict
a sufficient set of objects for a planning problem is a simple, powerful, and
general mechanism for planning in large instances. Video:
https://youtu.be/FWsVJc2fvCE Code: https://git.io/JIsqX
