A Simple Approach for Visual Room Rearrangement:
3D Mapping and Semantic Search
Brandon Trabucco ¹ , Gunnar Sigurdsson ² , Robinson Piramuthu ² ,
Gaurav S. Sukhatme ² ³ , Ruslan Salakhutdinov ¹
¹ Carnegie Mellon University , ² Amazon Alexa AI , ³ University of Southern California
Abstract
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 15.11%, using only 2.7% as many samples from the environment.
