[2605.08330] Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning
[Submitted on 8 May 2026]
Title:Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning
View a PDF of the paper titled Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning, by Karolina \'Zr\'obek and 4 other authors
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Abstract:We present a hierarchical language-driven framework for robotic task and motion planning to improve natural, intuitive human-robot interaction in service and assistance scenarios. The proposed system employs two large language model (LLM) modules: a high-level planning agent and a low-level spatial reasoning sub-module. The primary agent processes natural language commands and generates action sequences using a ReAct-style prompt, interacting with tools for object perception and manipulation (e.g., pick, place, release). For precise spatial placement, such as interpreting "place the mug next to the plate", a separate sub-prompting module handles 3D reasoning based on object geometry and scene layout. The system integrates YOLOX-GDRNet for object detection and pose estimation, along with a motion execution stub. We evaluated the system in 24 test scenarios, ranging from simple spatial commands to high-level instructions and infeasible requests. The system achieved an overall task success rate of 86%.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.08330 [cs.RO]
(or arXiv:2605.08330v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.08330
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Paweł Gajewski [view email] [v1] Fri, 8 May 2026 17:36:13 UTC (1,574 KB)
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