Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand. Decades of hierarchical planning techniques have used domain-specific temporal action abstractions to support efficient and accurate planning, almost always relying on human priors and domain knowledge to decompose hard tasks into smaller subproblems appropriate for a goal or set of goals. This paper describes Ada (Action Domain Acquisition), a framework for automatically constructing task-specific planning representations using task-general background knowledge from language models (LMs). Starting with a general-purpose hierarchical planner and a low-level goal-conditioned policy, Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks. On two language-guided interactive planning benchmarks (Mini Minecraft and ALFRED Household Tasks), Ada strongly outperforms other approaches that use LMs for sequential decision-making, offering more accurate plans and better generalization to complex tasks.

翻译：现实世界中的有效规划不仅需要世界知识，更需要利用这些知识构建当前任务的恰当表示。数十年的层次化规划技术一直采用领域特定的时序动作抽象来支持高效准确的规划，这些技术几乎完全依赖人类先验知识和领域知识，将复杂任务分解为适合特定目标的若干子问题。本文提出Ada（动作领域获取）框架，该框架利用语言模型中的任务通用背景知识，自动构建任务特定的规划表示。从通用层次化规划器和低级目标条件策略出发，Ada通过交互式学习，为特定规划任务领域构建与规划器兼容的高级动作抽象库和对应的低级控制器。在两项语言引导的交互式规划基准测试（Mini Minecraft和ALFRED家庭任务）中，Ada在利用语言模型进行序列决策方面显著优于其他方法，展现出更准确的规划能力以及对复杂任务更强的泛化性能。