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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2605.08330 |
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| _version_ | 1866909028296163328 |
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| author | Źróbek, Karolina Pulli, Tessa Gajewski, Paweł Gonzalez, Antonio Galiza Cerdeira Indurkhya, Bipin |
| author_facet | Źróbek, Karolina Pulli, Tessa Gajewski, Paweł Gonzalez, Antonio Galiza Cerdeira Indurkhya, Bipin |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08330 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning Źróbek, Karolina Pulli, Tessa Gajewski, Paweł Gonzalez, Antonio Galiza Cerdeira Indurkhya, Bipin Robotics 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%. |
| title | Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning |
| topic | Robotics |
| url | https://arxiv.org/abs/2605.08330 |