arXiv cs.AI· Bastien Dussard (LAAS-RIS, LAAS), Aur{\'e}lie Clodic (LAAS-IDEA, LAAS-RIS, LAAS), Guillaume Sarthou (LAAS-RIS, EPE UT, LAAS)·· 10 小时前AI 评分21
URDF2Ontology：将机器人 URDF 文件自动转换为 OWL 本体的神经符号框架
Taxonomy-Agnostic Neurosymbolic Grounding of Robot Kinematics
AI 导读
URDF2Ontology 是一个与分类体系无关的神经符号框架，可将原始 URDF 文件自动转换为填充完整的 OWL 本体。它利用 LLM 做定向语义推理，配合迭代式图剪枝机制防止上下文窗口饱和，并用形态感知的双轨架构分别处理分支装配与串联肢体。在多种异构机器人上的评估显示其模式对齐良好、结构提取有效，并显著降低人工领域建模开销。
正文 · 原文
Abstract:While symbolic reasoning for task allocation requires grounding a robot's physical capabilities, standard Unified Robot Description Format (URDF) files provide only low-level kinematics without the formal semantic context needed for reasoning. We present URDF2Ontology, an automated, taxonomy-agnostic neurosymbolic framework that transforms raw URDF files into populated, grounded OWL ontologies. It leverages a Large Language Model (LLM) for targeted semantic reasoning, coupled with an iterative sequential graph-purging mechanism to prevent context window saturation. To process complex kinematic structures, it employs a morphology-aware dual-track architecture featuring a bottom-up Tree Track for branching assemblies and a top-down Chain Track for serial limbs. Guided by neurosymbolic guardrails, the framework extracts functional sub-graphs and asserts populated ontologies aligned with arbitrary user-provided taxonomies. Empirical evaluation across a diverse fleet of heterogeneous robots demonstrates strong schema alignment, valid structural extraction, and a significant reduction in manual domain authoring overhead.
| Subjects: | Artificial Intelligence (cs.AI); Robotics (cs.RO) |
| Cite as: | arXiv:2611.13850 [cs.AI] |
| (or arXiv:2611.13850v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13850 arXiv-issued DOI via DataCite (pending registration) |
|
| Journal reference: | 4th AAAI Fall Symposium on Unifying Representations for Robot Application Development, Nov 2026, Arlington, VA, United States. pp.895-903 |
Submission history
From: Bastien Dussard [view email] [via CCSD proxy]
[v1]
Tue, 10 Nov 2026 10:11:30 UTC (2,144 KB)
来源：arXiv cs.AI · arxiv.org