Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents-猫眼课题宝

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发表时间：

2022-01

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ArXiv

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作者：

Wenlong Huang;P. Abbeel;Deepak Pathak;Igor Mordatch

大型语言模型（LLM）学习的世界知识可以用于交互式环境中吗？在本文中，我们研究了以自然语言表达的高级别任务（例如，“做早餐”），到一组选定的可操作步骤（例如，“打开冰箱”）。虽然之前的工作主要集中在学习如何采取行动的明确的一步一步的例子，我们惊讶地发现，如果预先训练的LM足够大，并得到适当的提示，他们可以有效地将高级任务分解为中级计划，而无需任何进一步的培训。然而，LLM天真地制定的计划往往不能精确地映射到可接受的行动。我们提出了一个程序，现有的示威活动的条件和语义翻译的计划，可受理的行动。我们在最近的VirtualHome环境中的评估表明，所得到的方法大大提高了LLM基线的可执行性。进行的人类评估揭示了可执行性和正确性之间的权衡，但显示出从语言模型中提取可操作知识的有希望的迹象。网址：https://huangwl18.github.io/language-planner

Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g."make breakfast"), to a chosen set of actionable steps (e.g."open fridge"). While prior work focused on learning from explicit step-by-step examples of how to act, we surprisingly find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training. However, the plans produced naively by LLMs often cannot map precisely to admissible actions. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions. Our evaluation in the recent VirtualHome environment shows that the resulting method substantially improves executability over the LLM baseline. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models. Website at https://huangwl18.github.io/language-planner

Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

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