PLanAR：面向机器人操作的规划语言基础智能推理
PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation
AI总结：
提出PLanAR框架，通过规划语言接口定义VLM推理空间，实现开放词汇的长时域机器人操作，并支持逐步验证与重规划。
摘要：
近期视觉-语言模型（VLM）的进步推动了真实世界机器人操作的进展。然而，非结构化环境中的长时域操作要求VLM推理变化的场景状态、动作约束和执行结果，这仅靠自然语言推理仍然困难。我们提出PLanAR，一个规划语言基础的机器人智能体框架，用于开放词汇的长时域操作。PLanAR使用规划语言接口定义VLM推理空间：对象谓词表示场景状态，动作模式指定具有前提条件和效果的机器人技能，符号规划提供可执行的中间表示。该接口支持逐步验证：在每个动作之后，PLanAR利用机载观测检查预期符号效果是否实现，使基于VLM的智能体能够更新任务状态、检测失败，并在执行偏离预期时重新规划。在多种机器人形态、VLM后端以及包括堆叠、填字游戏和长时域厨房工作流程的任务中，PLanAR展示了强大的真实世界能力，同时揭示了当前VLM在具身推理中的关键局限性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
