  We introduce Housekeep, a benchmark to evaluate commonsense reasoning in the
home for embodied AI. In Housekeep, an embodied agent must tidy a house by
rearranging misplaced objects without explicit instructions specifying which
objects need to be rearranged. Instead, the agent must learn from and is
evaluated against human preferences of which objects belong where in a tidy
house. Specifically, we collect a dataset of where humans typically place
objects in tidy and untidy houses constituting 1799 objects, 268 object
categories, 585 placements, and 105 rooms. Next, we propose a modular baseline
approach for Housekeep that integrates planning, exploration, and navigation.
It leverages a fine-tuned large language model (LLM) trained on an internet
text corpus for effective planning. We show that our baseline agent generalizes
to rearranging unseen objects in unknown environments. See our webpage for more
details: https://yashkant.github.io/housekeep/
