  This paper introduces a novel method for determining the best room to place
an object in, for embodied scene rearrangement. While state-of-the-art
approaches rely on large language models (LLMs) or reinforcement learned (RL)
policies for this task, our approach, CLIPGraphs, efficiently combines
commonsense domain knowledge, data-driven methods, and recent advances in
multimodal learning. Specifically, it (a)encodes a knowledge graph of prior
human preferences about the room location of different objects in home
environments, (b) incorporates vision-language features to support multimodal
queries based on images or text, and (c) uses a graph network to learn
object-room affinities based on embeddings of the prior knowledge and the
vision-language features. We demonstrate that our approach provides better
estimates of the most appropriate location of objects from a benchmark set of
object categories in comparison with state-of-the-art baselines
