  There has been a significant research interest in employing large language
models to empower intelligent robots with complex reasoning. Existing work
focuses on harnessing their abilities to reason about the histories of their
actions and observations. In this paper, we explore a new dimension in which
large language models may benefit robotics planning. In particular, we propose
Statler, a framework in which large language models are prompted to maintain an
estimate of the world state, which are often unobservable, and track its
transition as new actions are taken. Our framework then conditions each action
on the estimate of the current world state. Despite being conceptually simple,
our Statler framework significantly outperforms strong competing methods (e.g.,
Code-as-Policies) on several robot planning tasks. Additionally, it has the
potential advantage of scaling up to more challenging long-horizon planning
tasks.
