TidyBot：基于大型语言模型的个性化机器人辅助

摘要

为了让机器人能够有效地个性化物理辅助，它必须学习用户偏好，这些偏好可以被普遍地应用到未来的场景中。在这项工作中，我们研究了利用机器人个性化进行家庭清理，机器人可以通过捡起物品并把它们放好来整理房间。一个关键挑战是确定每个物品放置的适当位置，因为人们的偏好可能会因个人口味或文化背景而大不相同。例如，一个人可能喜欢把衬衫放在抽屉里，而另一个人可能更喜欢放在架子上。我们的目标是构建系统，可以通过与特定人的先前互动仅仅学习少量示例来学习这种偏好。我们展示了机器人可以将基于语言的规划和感知与大型语言模型（LLMs）的少样本摘要能力相结合，推断出广泛适用于未来互动的用户偏好。这种方法实现了快速适应，并在我们的基准数据集中对未见过的物品达到了91.2%的准确率。我们还在一个名为TidyBot的真实世界移动操作器上展示了我们的方法，成功地在真实世界测试场景中整理了85.0%的物品。

English

For a robot to personalize physical assistance effectively, it must learn user preferences that can be generally reapplied to future scenarios. In this work, we investigate personalization of household cleanup with robots that can tidy up rooms by picking up objects and putting them away. A key challenge is determining the proper place to put each object, as people's preferences can vary greatly depending on personal taste or cultural background. For instance, one person may prefer storing shirts in the drawer, while another may prefer them on the shelf. We aim to build systems that can learn such preferences from just a handful of examples via prior interactions with a particular person. We show that robots can combine language-based planning and perception with the few-shot summarization capabilities of large language models (LLMs) to infer generalized user preferences that are broadly applicable to future interactions. This approach enables fast adaptation and achieves 91.2% accuracy on unseen objects in our benchmark dataset. We also demonstrate our approach on a real-world mobile manipulator called TidyBot, which successfully puts away 85.0% of objects in real-world test scenarios.

TidyBot：基于大型语言模型的个性化机器人辅助

TidyBot: Personalized Robot Assistance with Large Language Models

摘要

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