TidyBot：搭載大型語言模型的個人化機器人助理

摘要

為了有效地個性化物理協助，機器人必須學習用戶偏好，並將其應用於未來情境。在這項研究中，我們探討了使用機器人個性化家庭清潔，讓機器人可以通過收拾物品來整理房間。一個關鍵挑戰是確定每個物品應該放在哪個位置，因為人們的偏好可能會因個人口味或文化背景而大不相同。例如，一個人可能喜歡把襯衫放在抽屜裡，而另一個人可能更喜歡放在架子上。我們的目標是建立可以從與特定人互動的先前例子中學習此類偏好的系統。我們展示了機器人如何結合基於語言的規劃和感知，以及大型語言模型（LLMs）的少量示例摘要能力，來推斷出廣泛適用於未來互動的用戶偏好。這種方法實現了快速適應，在我們的基準數據集中對未見物品達到了91.2%的準確率。我們還在一個名為TidyBot的現實世界移動機械手上展示了我們的方法，該機器人在真實測試情境中成功整理了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：搭載大型語言模型的個人化機器人助理

TidyBot: Personalized Robot Assistance with Large Language Models

摘要

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