While commonsense knowledge may suffice for virtual agents, embodied robots interacting with humans require grounded and semantically rich representations of both their environment and their own physical embodiment. In cognitive robotics, ontologies are effective for integrating such heterogeneous knowledge to enable explainable reasoning, even during continuous knowledge updates. Yet, their manual construction remains a bottleneck. We present a preliminary approach for the automatic generation of robot semantic abstractions by transforming Unified Robot Description Format (URDF) models into populated ontologies. Although URDF files provide structural and kinematic descriptions, their identifiers often require commonsense interpretation to recover meaningful semantics, a task at which Large Language Models (LLMs) excel. Our pipeline leverages LLMs to infer semantic relationships by prompting them with concepts from an existing ontology, ensuring the final classification remains aligned with the formal model. To improve reliability, the pipeline combines majority voting across multiple LLM queries along with syntactic and schema-level validation to ensure that generated outputs conform to the expected representation format and ontology constraints. We evaluate the approach on multiple robot descriptions and discuss the generated abstractions. Initial results indicate that the proposed method can effectively bridge the gap between low-level robot descriptions and the structured, grounded knowledge representations required for human-robot interaction.

翻译：常识性知识或许足以支撑虚拟智能体，但与人类交互的具身机器人需要对其环境及自身物理形态具备具身化、语义丰富的表征。在认知机器人学中，本体论能有效整合这类异质知识，即使在持续知识更新过程中也能支持可解释推理。然而，手动构建本体仍是瓶颈。我们提出一种初步方法，通过将统一机器人描述格式（URDF）模型转化为已实例化的本体，自动生成机器人的语义抽象。尽管URDF文件提供了结构和运动学描述，但其标识符通常需要常识性解读才能恢复有意义的语义，而大型语言模型（LLM）正擅长此任务。本文流程利用LLM，通过用现有本体中的概念对其进行提示来推断语义关系，确保最终分类与形式化模型保持一致。为提升可靠性，该流程结合了跨多次LLM查询的多数投票机制，以及句法与模式层级验证，确保生成输出符合预期的表征格式和本体约束。我们在多种机器人描述上评估了该方法，并讨论了生成的抽象结果。初步结果表明，所提方法能有效弥合低层次机器人描述与人类-机器人交互所需的结构化、具身化知识表征之间的鸿沟。