论文标题
管家：使用常识性推理整理虚拟家庭
Housekeep: Tidying Virtual Households using Commonsense Reasoning
论文作者
论文摘要
我们介绍了家政管理，这是一种评估体现AI家中常识性推理的基准。在管家中，具体的代理必须通过重新安排放错位置的物体来整理房屋，而无需明确说明指定需要重新安排哪些物体。取而代之的是，代理必须根据人类属于哪些物体属于整洁的房屋的偏好进行学习和评估。具体而言，我们收集一个数据集，其中人类通常将对象放在整洁和不整洁的房屋中，构成1799个对象，268个对象类别，585个位置和105个房间。接下来，我们提出了一种整合计划，探索和导航的模块化基线方法。它利用了在互联网文本语料库中训练的微调大语言模型（LLM）进行有效的计划。我们表明，我们的基线代理在未知环境中概括地重新安排看不见的对象。有关更多详细信息，请参见我们的网页：https：//yashkant.github.io/housekeep/
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/
