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arXiv · 2602.01662

PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation

Pengyuan Guo·Zhonghao Mai·Zhengtong Xu·Kaidi Zhang·Quan Khanh Luu·Heng Zhang·Zichen Miao·Arash Ajoudani·Zachary Kingston·Qiang Qiu·Yu She
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Abstract

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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Pengyuan Guo, Zhonghao Mai, Zhengtong Xu, Kaidi Zhang, Quan Khanh Luu, Heng Zhang, Zichen Miao, Arash Ajoudani, Zachary Kingston, Qiang Qiu, Yu She. 2026-02-02. PLanAR: Planning-Language-Grounded Agentic Reasoning for Robot Manipulation. https://arxiv.org/abs/2602.01662

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