This work addresses the problem of long-horizon task planning with the Large
Language Model (LLM) in an open-world household environment. Existing works
fail to explicitly track key objects and attributes, leading to erroneous
decisions in long-horizon tasks, or rely on highly engineered state features
and feedback, which is not generalizable. We propose a novel, expandable state
representation that provides continuous expansion and updating of object
attributes from the LLM's inherent capabilities for context understanding and
historical action reasoning. Our proposed representation maintains a
comprehensive record of an object's attributes and changes, enabling robust
retrospective summary of the sequence of actions leading to the current state.
This allows enhanced context understanding for decision-making in task
planning. We validate our model through experiments across simulated and
real-world task planning scenarios, demonstrating significant improvements over
baseline methods in a variety of tasks requiring long-horizon state tracking
and reasoning.

本研究通过使用大规模语言模型（LLM）来解决开放式家庭环境中长期任务规划的问题。我们提出了一种新颖的可扩展状态表示方法，利用 LLM 的上下文理解和历史行为推理能力来持续扩展和更新对象属性。我们的模型在模拟和现实世界的任务规划场景中验证，表明在需要长期跟踪和推理状态的各种任务中，与基准方法相比取得了显著的改进。

LLM-State：开放世界长期规划的可扩展状态表示

LLM-State: Expandable State Representation for Long-horizon Task  Planning in the Open World

An interactive robot framework accomplishes long-horizon task planning and
can easily generalize to new goals or distinct tasks, even during execution.
However, most traditional methods require predefined module design, which makes
it hard to generalize to different goals. Recent large language model based
approaches can allow for more open-ended planning but often require heavy
prompt engineering or domain-specific pretrained models. To tackle this, we
propose a simple framework that achieves interactive task planning with
language models. Our system incorporates both high-level planning and low-level
function execution via language. We verify the robustness of our system in
generating novel high-level instructions for unseen objectives and its ease of
adaptation to different tasks by merely substituting the task guidelines,
without the need for additional complex prompt engineering. Furthermore, when
the user sends a new request, our system is able to replan accordingly with
precision based on the new request, task guidelines and previously executed
steps. Please check more details on our this https URL
and this https URL

一个互动机器人框架可以完成长期任务规划，并能在执行过程中轻松推广到新目标或不同任务。通过语言模型实现了高级规划和低级功能执行的系统，实现了对未知目标的新颖高级指令的生成，并通过仅替换任务指南来适应不同任务，而无需复杂的提示工程。此外，当用户发送新请求时，系统能够根据新请求、任务指南和先前执行步骤精确地重新规划。

语言模型与交互式任务规划

Interactive Task Planning with Language Models

Long-horizon task planning is essential for the development of intelligent
assistive and service robots. In this work, we investigate the applicability of
a smaller class of large language models (LLMs), specifically GPT-2, in robotic
task planning by learning to decompose tasks into subgoal specifications for a
planner to execute sequentially. Our method grounds the input of the LLM on the
domain that is represented as a scene graph, enabling it to translate human
requests into executable robot plans, thereby learning to reason over
long-horizon tasks, as encountered in the ALFRED benchmark. We compare our
approach with classical planning and baseline methods to examine the
applicability and generalizability of LLM-based planners. Our findings suggest
that the knowledge stored in an LLM can be effectively grounded to perform
long-horizon task planning, demonstrating the promising potential for the
future application of neuro-symbolic planning methods in robotics.

本文研究了如何通过 GPT-2 等大型语言模型将人类请求转化为机器人可执行的计划，并探讨了 LLM 的长期任务规划的适用性和通用性，结果表明 LLM 可有效地执行长期任务规划，展示了神经符号规划方法在机器人领域的应用前景。