Housekeep: Tidying Virtual Households Using Commonsense Reasoning

9Citations
Citations of this article
31Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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 find that our baseline planner generalizes to some extent when rearranging objects in unknown environments. See our webpage for code, data and more details: https://yashkant.github.io/housekeep/.

Cite

CITATION STYLE

APA

Kant, Y., Ramachandran, A., Yenamandra, S., Gilitschenski, I., Batra, D., Szot, A., & Agrawal, H. (2022). Housekeep: Tidying Virtual Households Using Commonsense Reasoning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13699 LNCS, pp. 355–373). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19842-7_21

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free