Abstract
The deployment of assistive robots in everyday life scenarios and their capability of providing an effective and useful support for independent living is an open and challenging research problem. The development of suitable robot control systems requires effective solutions for addressing issues concerning performance, reliability, flexibility and proactivity. In the context of daily-living assistance, we advance a recently developed AI-based cognitive architecture by integrating learning capabilities with the aim of extracting behavioral models of user. Such models allows the resulting cognitive system to know the daily-living habits of a user and make better assistive decisions.
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Acknowledgements
Authors are partially supported by Italian M.U.R. under project “SI-ROBOTICS: SocIal ROBOTICS for active and healthy ageing” (PON – Ricerca e Innovazione 2014-2020 – G.A. ARS01_01120). Authors are also supported by the EU project TAILOR “Foundations of Trustworthy AI - Integrating Learning, Optimisation and Reasoning” G.A. 952215.)
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Pantaleoni, M., Cesta, A., Umbrico, A., Orlandini, A. (2022). Learning User Habits to Enhance Robotic Daily-Living Assistance. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13817. Springer, Cham. https://doi.org/10.1007/978-3-031-24667-8_15
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