  Physically rearranging objects is an important capability for embodied
agents. Visual room rearrangement evaluates an agent's ability to rearrange
objects in a room to a desired goal based solely on visual input. We propose a
simple yet effective method for this problem: (1) search for and map which
objects need to be rearranged, and (2) rearrange each object until the task is
complete. Our approach consists of an off-the-shelf semantic segmentation
model, voxel-based semantic map, and semantic search policy to efficiently find
objects that need to be rearranged. On the AI2-THOR Rearrangement Challenge,
our method improves on current state-of-the-art end-to-end reinforcement
learning-based methods that learn visual rearrangement policies from 0.53%
correct rearrangement to 16.56%, using only 2.7% as many samples from the
environment.
