Statler: State-Maintaining Language Models for Embodied Reasoning
Authors: name: Takuma Yoneda, name: Jiading Fang, name: Peng Li, name: Huanyu Zhang, name: Tianchong Jiang, name: Shengjie Lin, name: Ben Picker, name: David Yunis, name: Hongyuan Mei, name: Matthew R. Walter

Large language models (LLMs) provide a promising tool that enable robots to
perform complex robot reasoning tasks. However, the limited context window of
contemporary LLMs makes reasoning over long time horizons difficult. Embodied
tasks such as those that one might expect a household robot to perform
typically require that the planner consider information acquired a long time
ago (e.g., properties of the many objects that the robot previously encountered
in the environment). Attempts to capture the world state using an LLM's
implicit internal representation is complicated by the paucity of task- and
environment-relevant information available in a robot's action history, while
methods that rely on the ability to convey information via the prompt to the
LLM are subject to its limited context window. In this paper, we propose
Statler, a framework that endows LLMs with an explicit representation of the
world state as a form of ``memory'' that is maintained over time. Integral to
Statler is its use of two instances of general LLMs -- a world-model reader and
a world-model writer -- that interface with and maintain the world state. By
providing access to this world state ``memory'', Statler improves the ability
of existing LLMs to reason over longer time horizons without the constraint of
context length. We evaluate the effectiveness of our approach on three
simulated table-top manipulation domains and a real robot domain, and show that
it improves the state-of-the-art in LLM-based robot reasoning. Project website:
https://statler-lm.github.io/