Large language models (LLMs) are accelerating the development of
language-guided robot planners. Meanwhile, symbolic planners offer the
advantage of interpretability. This paper proposes a new task that bridges
these two trends, namely, multimodal planning problem specification. The aim is
to generate a problem description (PD), a machine-readable file used by the
planners to find a plan. By generating PDs from language instruction and scene
observation, we can drive symbolic planners in a language-guided framework. We
propose a Vision-Language Interpreter (ViLaIn), a new framework that generates
PDs using state-of-the-art LLM and vision-language models. ViLaIn can refine
generated PDs via error message feedback from the symbolic planner. Our aim is
to answer the question: How accurately can ViLaIn and the symbolic planner
generate valid robot plans? To evaluate ViLaIn, we introduce a novel dataset
called the problem description generation (ProDG) dataset. The framework is
evaluated with four new evaluation metrics. Experimental results show that
ViLaIn can generate syntactically correct problems with more than 99% accuracy
and valid plans with more than 58% accuracy.

提出了一种将语言模型和符号规划器相结合的新框架，通过生成语言指令和场景观察的问题描述 (PD) 来驱动符号规划器，实现语言引导的机器人规划，实验结果显示该框架能够以超过 99% 的准确率生成句法正确的问题描述和超过 58% 的准确率生成有效的机器人规划。

机器人任务规划的视觉语言解释器

Vision-Language Interpreter for Robot Task Planning

While Large Language Models (LLMs) can solve many NLP tasks in zero-shot
settings, applications involving embodied agents remain problematic. In
particular, complex plans that require multi-step reasoning become difficult
and too costly as the context window grows. Planning requires understanding the
likely effects of one's actions and identifying whether the current environment
satisfies the goal state. While symbolic planners find optimal solutions
quickly, they require a complete and accurate representation of the planning
problem, severely limiting their use in practical scenarios. In contrast,
modern LLMs cope with noisy observations and high levels of uncertainty when
reasoning about a task. Our work presents LLM Dynamic Planner (LLM-DP): a
neuro-symbolic framework where an LLM works hand-in-hand with a traditional
planner to solve an embodied task. Given action-descriptions, LLM-DP solves
Alfworld faster and more efficiently than a naive LLM ReAct baseline.

大型语言模型与符号规划器结合的神经符号框架，为解决涉及体验代理的复杂计划任务提供更快速和高效的解决方案。

LLM 动态规划

Dynamic Planning with a LLM

Automated plot generation is the challenge of generating a sequence of events
that will be perceived by readers as the plot of a coherent story. Traditional
symbolic planners plan a story from a goal state and guarantee logical causal
plot coherence but rely on a library of hand-crafted actions with their
preconditions and effects. This closed world setting limits the length and
diversity of what symbolic planners can generate. On the other hand,
pre-trained neural language models can generate stories with great diversity,
while being generally incapable of ending a story in a specified manner and can
have trouble maintaining coherence. In this paper, we present an approach to
story plot generation that unifies causal planning with neural language models.
We propose to use commonsense knowledge extracted from large language models to
recursively expand a story plot in a backward chaining fashion. Specifically,
our system infers the preconditions for events in the story and then events
that will cause those conditions to become true. We performed automatic
evaluation to measure narrative coherence as indicated by the ability to answer
questions about whether different events in the story are causally related to
other events. Results indicate that our proposed method produces more coherent
plotlines than several strong baselines.

本文提出了一种将因果规划与神经语言模型统一起来的故事情节生成方法，该方法利用从大型语言模型中提取的常识知识以递归反向链接的方式扩展故事情节，并通过自动化评估证明相对于其他强基线方法它能生成更具连贯性的情节。