Personalised Explanations in Long-term Human-Robot Interactions
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918082829615104 |
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| author | Gebellí, Ferran Garrell, Anaís Habekost, Jan-Gerrit Lemaignan, Séverin Wermter, Stefan Ros, Raquel |
| author_facet | Gebellí, Ferran Garrell, Anaís Habekost, Jan-Gerrit Lemaignan, Séverin Wermter, Stefan Ros, Raquel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03049 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalised Explanations in Long-term Human-Robot Interactions Gebellí, Ferran Garrell, Anaís Habekost, Jan-Gerrit Lemaignan, Séverin Wermter, Stefan Ros, Raquel Robotics Artificial Intelligence Human-Computer Interaction 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. |
| title | Personalised Explanations in Long-term Human-Robot Interactions |
| topic | Robotics Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.03049 |