Learning to Explore, Navigate and Interact for Visual Room Rearrangement

Intelligent agents for visual room rearrangement aim to reach a goal room configuration from a cluttered room configuration via a sequence of interactions. For successful visual room rearrangement, the agents need to learn to explore, navigate and interact within the surrounding environments. Contemporary methods for visual room rearrangement display unsatisfactory performance even with stateof-the-art techniques for embodied AI. One of the causes for the low performance arises from the expensive cost of learning in an end-to-end manner. To overcome the limitation, we design a three-phased modular architecture (TMA) for visual room rearrangement. TMA performs visual room rearrangement in three phases: the exploration phase, the inspection phase, and the rearrangement phase. The proposed TMA maximizes the performance by placing the learning modules along with hand-crafted feature engineering modules—retaining the advantage of learning while reducing the cost of learning.