title: Statler: State-Maintaining Language Models for Embodied Reasoning
summary: 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/
paper_authors._id: 64a2337dd94c3f9ccec83524
paper_authors.name: Takuma Yoneda
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paper_authors.fullname: Takuma Yoneda
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paper_authors._id: 64a2337dd94c3f9ccec83525
paper_authors.name: Jiading Fang
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paper_authors.fullname: Jiading Fang
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paper_authors.name: Peng Li
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paper_authors.fullname: Peng Li
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paper_authors.name: Huanyu Zhang
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paper_authors.name: Tianchong Jiang
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paper_authors.name: Shengjie Lin
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paper_authors.name: Ben Picker
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paper_authors.fullname: Ben Picker
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paper_authors.user: benpicker
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paper_authors.name: David Yunis
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paper_authors.fullname: David
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paper_authors.user: dyunis
paper_authors._id: 64a2337dd94c3f9ccec8352c
paper_authors.name: Hongyuan Mei
paper_authors._id: 64a2337dd94c3f9ccec8352d
paper_authors.name: Matthew R. Walter
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paper_authors.fullname: Matthew Walter
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paper_title: Statler: State-Maintaining Language Models for Embodied Reasoning
paper_summary: 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/
paper_ai_summary: Statler framework enhances LLMs' ability to reason over long time horizons by integrating an explicit world state memory using two LLM instances.
paper_ai_keywords: Large language models LLMs robot reasoning context window world state memory world-model reader world-model writer table-top manipulation real robot domain