让机器人通过计划语言一步步验证执行，实现复杂任务的自主操作。
PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation
- 用物体状态、动作模板和符号化计划构建可验证的推理空间。
- 每步操作后通过视觉反馈检查预期效果，失败时自动重规划。
- 适用于多种机器人和任务，揭示当前视觉语言模型的局限性。
视觉语言模型（VLMs）的进步推动了真实世界机器人操作的发展。然而，在非结构化环境中进行长时程操作仍需模型对场景状态变化、动作约束及执行结果进行推理，仅靠自然语言推理难以应对。我们提出PLanAR，一种基于计划语言的机器人智能体框架，支持开放词汇、长时程操作。该框架通过计划语言接口定义VLM的推理空间：物体谓词表示场景状态，动作模式定义带前提与效果的机器人技能，符号化计划提供可执行的中间表示。这一接口支持分步验证：每次动作后，系统利用本地观测检查预期符号效果是否达成，从而更新任务状态、检测失败并重新规划。在不同机器人平台、VLM后端及多项任务（包括堆叠、填字游戏、长流程厨房任务）中，PLanAR展现出强大的现实能力，同时揭示了当前VLM在具身推理中的关键不足。
原文摘要 · Abstract (English)
Recent advances in vision-language models (VLMs) have enabled increasing progress in real-world robot manipulation. However, long-horizon manipulation in unstructured environments requires VLMs to reason about changing scene states, action constraints, and execution outcomes, which remains difficult with natural language reasoning alone. We present PLanAR, a planning-language-grounded robot agent framework for open-vocabulary, long-horizon manipulation. PLanAR uses a planning-language interface to define the VLM reasoning space: object predicates represent scene states, action schemas specify robot skills with preconditions and effects, and symbolic plans provide executable intermediate representations. This interface enables stepwise verification: after each action, PLanAR uses onboard observations to check whether the expected symbolic effects have been achieved, allowing the VLM-based agent to update task states, detect failures, and replan when execution deviates from expectation. Across robot embodiments, VLM backends, and tasks including stacking, crossword solving, and long-horizon kitchen workflows, PLanAR demonstrates strong real-world capability while revealing key limitations of current VLMs in embodied reasoning.
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