Large language models (LLMs)  have demonstrated remarkable zero-shot generalization abilities: state-of-the-art chatbots can provide plausible answers to many common questions that arise in daily life.
However, so far, LLMs cannot reliably solve long-horizon robot planning problems.
By contrast, classical planners, once a problem is given in a formatted way, can use efficient search algorithms to quickly identify correct, or even optimal, plans.
In an effort to get the best of both worlds, this paper introducesLLM+P, the first framework that incorporates the strengths of classical planners into LLMs.LLM+Ptakes in a natural language description of a planning problem, then returns a correct (or optimal)  plan for solving that problem in natural language.LLM+Pdoes so by first converting the language description into a file written in the planning domain definition language (PDDL) , then leveraging classical planners to quickly find a solution, and then translating the found solution back into natural language. Along withLLM+P, we define a diverse set of different benchmark problems taken from robot planning scenarios.
Via a comprehensive set of experiments on these benchmark problems, we find thatLLM+Pis able to provideoptimalsolutions for most problems, while LLMs fail to provide even feasible plans for most problems.
We also showLLM+Penables a home robot to solve a complex manipulation task that is specified by the user in natural language.111The code and results are publicly available athttps://github.com/Cranial-XIX/llm-pddl.git.