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

TL;DR

This paper introduces a hybrid approach combining ontologies and large language models to generate semantically grounded, clear, and adaptive explanations for robots, enhancing human-robot interaction and collaboration.

Contribution

It presents a novel framework integrating ontology-based reasoning with LLMs for producing semantically grounded and natural robot explanations, validated through empirical laboratory studies.

Findings

Significant improvement in explanation clarity and brevity

Effective adaptation of explanations based on user feedback

Maintained semantic accuracy in explanations

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.