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In such a scenario, people\u2019s novelty effect connected to the use of an attractive technology is destined to wear off and, consequently, negatively affects the success of the service robotics application. For this reason, providing personalised natural interaction while people access its services is fundamental for increasing users\u2019 engagement and, consequently, their loyalty. In this paper, we present a novel robotic system that is able to not only provide a recommanded service (from the ordering to the preparation of a drink), but also personalise the verbal and non-verbal interaction. In particular, we described the developed three-layer ROS architecture integrating a perception layer managing the processing of different social signals, a decision-making layer for handling multi-party interactions, and an execution layer controlling the behaviour of a complex robot composed of arms and a face. Finally, user modelling through a beliefs layer allows for personalized interaction. We also present the results of both people\u2019s interaction, experience and performances in a real user case. The user study involved 116 participants and showed that BRILLO is considered an easy-to-use and attractive system by the users.<\/jats:p>","DOI":"10.1007\/s12369-025-01228-3","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T10:16:35Z","timestamp":1741860995000},"page":"2147-2163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["BRILLO: Personalised HRI with a Bartender Robot"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1362-8799","authenticated-orcid":false,"given":"Alessandra","family":"Rossi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3379-1756","authenticated-orcid":false,"given":"Silvia","family":"Rossi","sequence":"additional","affiliation":[]},{"given":"Maria Di","family":"Maro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8635-1623","authenticated-orcid":false,"given":"Antonio","family":"Origlia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"1228_CR1","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1007\/978-3-319-98192-5_3","volume-title":"The semantic web: ESWC 2018 satellite events","author":"M Atzeni","year":"2018","unstructured":"Atzeni M, Reforgiato Recupero D (2018) Deep learning and sentiment analysis for human-robot interaction. 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