Ontological grounding for sound and natural robot explanations via large language models

• URL: http://arxiv.org/abs/2602.13800v1
• Date: Sat, 14 Feb 2026 14:23:11 GMT
• Title: Ontological grounding for sound and natural robot explanations via large language models
• Abstract summary: This paper presents a hybrid framework that blends ontology-based reasoning with large language models (LLMs) to produce semantically grounded and natural robot explanations.<n>Ontologies ensure logical consistency and domain grounding, while LLMs provide fluent, context-aware and adaptive language generation.<n>We integrate a state-of-the-art algorithm for retrieving and constructing static contrastive-based narratives with an LLM agent that uses them to produce concise, clear, interactive explanations.
• Score: 6.358448988089591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Building effective human-robot interaction requires robots to derive conclusions from their experiences that are both logically sound and communicated in ways aligned with human expectations. This paper presents a hybrid framework that blends ontology-based reasoning with large language models (LLMs) to produce semantically grounded and natural robot explanations. Ontologies ensure logical consistency and domain grounding, while LLMs provide fluent, context-aware and adaptive language generation. The proposed method grounds data from human-robot experiences, enabling robots to reason about whether events are typical or atypical based on their properties. We integrate a state-of-the-art algorithm for retrieving and constructing static contrastive ontology-based narratives with an LLM agent that uses them to produce concise, clear, interactive explanations. The approach is validated through a laboratory study replicating an industrial collaborative task. Empirical results show significant improvements in the clarity and brevity of ontology-based narratives while preserving their semantic accuracy. Initial evaluations further demonstrate the system's ability to adapt explanations to user feedback. Overall, this work highlights the potential of ontology-LLM integration to advance explainable agency, and promote more transparent human-robot collaboration.

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