{
  "id": 1165,
  "url": "https://arxiv.org/abs/2606.17073v1",
  "title": "Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF",
  "summary": "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 ",
  "authors": "Bastien Dussard, Guillaume Sarthou",
  "category": "research",
  "topics": "agents-autonomy,transparency,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-10T08:08:37.000Z",
  "fetched_at": "2026-07-14T14:15:03.616Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/1165",
  "original_url": "https://arxiv.org/abs/2606.17073v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}