Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF
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
Record details
Published: 10 June 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Transparency · Environment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
SkillAudit: Ground-Truth-Free Skill Evolution via Paired Trajectory Auditing
arXiv · 12 June 2026
Counterfactual Shapley Credit Assignment
arXiv cs.LG · 18 July 2026
A Decoupled Human-in-the-Loop System for Controlled Autonomy in Agentic Workflows
arXiv · 24 April 2026
Agentic Commerce World: An Auditable and Verifiable Environment for Vibe Commerce
arXiv cs.AI · 3 August 2026
Audio Spatially-Guided Fusion for Audio-Visual Navigation
arXiv · 2 April 2026
Emergence WebVoyager: Toward Consistent and Transparent Evaluation of (Web) Agents in The Wild
arXiv · 30 March 2026
How to cite this record
ethics.ai (10 June 2026), “Extracting Semantics: LLM-Guided Automatic Population of Robot Ontology from URDF,” evidence record 1165, https://ethics.ai/record/1165 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.