Long-horizon planning is dauntingly hard -- it requires modeling relevant aspects of the environment and searching over large, complex action spaces. \textit{Hierarchical planning} approaches make complex problems more tractable using temporal \textit{action abstractions}, decomposing hard tasks into smaller abstract subproblems that can be solved modularly. However, actually learning useful action abstractions has long posed significant challenges without human expert knowledge. Here, we introduce a system that leverages background information in language to learn a \textit{library of symbolic action abstractions and accompanying low-level policies} that can be composed to solve increasingly complex tasks. Our approach queries large language models (LLMs) as a prior for proposing useful symbolic action definitions, but integrates these proposals into a formal hierarchical planning system to ground and verify proposed actions. On two language-guided interactive planning domains (\textit{Mini Minecraft} and the \textit{ALFRED Household Tasks} benchmark), our approach far outperforms other baseline approaches that use LLMs in planning, enabling far more accurate planning and enable better generalization to more complex tasks.
Learning Grounded Action Abstractions from Language
Lionel Wong Jiayuan Mao Pratyusha Sharma Zachary S Siegel Jiahai Feng Noa Korneev Joshua B. Tenenbaum Jacob Andreas