Keywords: Embodied AI, Deep Learning, Object Rearrangement
TL;DR: A System For Exploring A Scene, Mapping Objects, and Rearranging Objects To A Visual Goal
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 16.56\%, using only 2.7\% as many samples from the environment.
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