  Robots will be expected to manipulate a wide variety of objects in complex
and arbitrary ways as they become more widely used in human environments. As
such, the rearrangement of objects has been noted to be an important benchmark
for AI capabilities in recent years. We propose NeRP (Neural Rearrangement
Planning), a deep learning based approach for multi-step neural object
rearrangement planning which works with never-before-seen objects, that is
trained on simulation data, and generalizes to the real world. We compare NeRP
to several naive and model-based baselines, demonstrating that our approach is
measurably better and can efficiently arrange unseen objects in fewer steps and
with less planning time. Finally, we demonstrate it on several challenging
rearrangement problems in the real world.
