Publication

Personalised explanations in long-term human-robot interactions

Conference Article

Conference

IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)

Edition

34th

Pages

775-782

Doc link

http://dx.doi.org/10.1109/RO-MAN63969.2025.11217900

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Download the digital copy of the doc pdf document

Authors

Projects associated

Abstract

In the field of Human-Robot Interaction (HRI), a fundamental challenge is to facilitate human understanding of robots. The emerging domain of eXplainable HRI (XHRI) investigates methods to generate explanations and evaluate their impact on human-robot interactions. Previous works have highlighted the need to personalise the level of detail of these explanations to enhance usability and comprehension. Our paper presents a framework designed to update and retrieve user knowledge-memory models, allowing for adapting the explanations' level of detail while referencing previously acquired concepts. Three architectures based on our proposed framework that use Large Language Models (LLMs) are evaluated in two distinct scenarios: a hospital patrolling robot and a kitchen assistant robot. Experimental results demonstrate that a two-stage architecture, which first generates an explanation and then personalises it, is the framework architecture that effectively reduces the level of detail only when there is related user knowledge.

Categories

service robots.

Author keywords

HRI, Explainable Robots

Scientific reference

F. Gebelli, A. Garrell Zulueta, J. Habekost, S. Lemaignan, S. Wermter and R. Ros. Personalised explanations in long-term human-robot interactions, 34th IEEE International Symposium on Robot and Human Interactive Communication, 2025, Eindhoven, Netherlands, pp. 775-782.