Classical planning approaches guarantee finding a set of actions that can
achieve a given goal state when possible, but require an expert to specify
logical action semantics that govern the dynamics of the environment.
Researchers have shown that Large Language Models (LLMs) can be used to
directly infer planning steps based on commonsense knowledge and minimal domain
information alone, but such plans often fail on execution. We bring together
the strengths of classical planning and LLM commonsense inference to perform
domain induction, learning and validating action pre- and post-conditions based
on closed-loop interactions with the environment itself. We propose PSALM,
which leverages LLM inference to heuristically complete partial plans emitted
by a classical planner given partial domain knowledge, as well as to infer the
semantic rules of the domain in a logical language based on environment
feedback after execution. Our analysis on 7 environments shows that with just
one expert-curated example plans, using LLMs as heuristic planners and rule
predictors achieves lower environment execution steps and environment resets
than random exploration while simultaneously recovering the underlying ground
truth action semantics of the domain.

提出使用经典规划和大型语言模型共同实施领域归纳、学习和验证动作的前后条件，利用 LLM 推理来启发性地完成经典规划器发出的部分计划，并根据执行后的环境反馈用逻辑语言推断领域的语义规则。通过对 7 个环境的分析表明，使用 LLMs 作为启发性规划器和规则预测器仅需一个专家精心策划的示例计划，就能够比随机探索更少的执行步骤和环境重置，并同时恢复领域的基本行动语义。

语言模型可以从环境反馈中推断经典计划器的动作语义

Language Models can Infer Action Semantics for Classical Planners from  Environment Feedback

The goal of motion understanding is to establish a reliable mapping between
motion and action semantics, while it is a challenging many-to-many problem. An
abstract action semantic (i.e., walk forwards) could be conveyed by
perceptually diverse motions (walk with arms up or swinging), while a motion
could carry different semantics w.r.t. its context and intention. This makes an
elegant mapping between them difficult. Previous attempts adopted
direct-mapping paradigms with limited reliability. Also, current automatic
metrics fail to provide reliable assessments of the consistency between motions
and action semantics. We identify the source of these problems as the
significant gap between the two modalities. To alleviate this gap, we propose
Kinematic Phrases (KP) that take the objective kinematic facts of human motion
with proper abstraction, interpretability, and generality characteristics.
Based on KP as a mediator, we can unify a motion knowledge base and build a
motion understanding system. Meanwhile, KP can be automatically converted from
motions and to text descriptions with no subjective bias, inspiring Kinematic
Prompt Generation (KPG) as a novel automatic motion generation benchmark. In
extensive experiments, our approach shows superiority over other methods. Our
code and data would be made publicly available at this https URL

运动理解的目标是建立动作语义与运动之间的可靠映射关系，通过使用运动的客观动力学事实作为中介，我们可以统一运动知识库并构建一个运动理解系统，这也激发了运动生成的新型自动基准测试。