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Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Adaptivity for All
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Speech, Music and Hearing, TMH.
Van Robotics, USA.
University of Naples Parthenope, Italy.
University of Cambridge, UK.
Number of Authors: 42023 (English)In: HRI 2023: Companion of the ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, p. 929-931Conference paper, Published paper (Refereed)
Abstract [en]

Adaptation and personalization are critical elements when modeling robot behaviors toward users in real-world settings. Multiple aspects of the user need to be taken into consideration in order to personalize the interaction, such as their personality, emotional state, intentions, and actions. While this information can be obtained a priori through self-assessment questionnaires or in realtime during the interaction through user profiling, behaviors and preferences can evolve in long-term interactions. Thus, gradually learning new concepts or skills (i.e., "lifelong learning") both for the users and the environment is crucial to adapt to new situations and personalize interactions with the aim of maintaining their interest and engagement. In addition, adapting to individual differences autonomously through lifelong learning allows for inclusive interactions with all users with varying capabilities and backgrounds. The third edition1 of the "Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)" workshop aims to gather and present interdisciplinary insights from a variety of fields, such as education, rehabilitation, elderly care, service and companion robots, for lifelong robot learning and adaptation to users, context, environment, and activities in long-term interactions. The workshop aims to promote a common ground among the relevant scientific communities through invited talks and indepth discussions via paper presentations, break-out groups, and a scientific debate. In line with the HRI 2023 conference theme, "HRI for all", our workshop theme is "adaptivity for all" to encourage HRI theories, methods, designs, and studies for lifelong learning, personalization, and adaptation that aims to promote inclusion and diversity in HRI.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2023. p. 929-931
Keywords [en]
Adaptation, Continual Learning, Diversity, Human-Robot Interaction, Inclusivity, Lifelong Learning, Long-Term Interaction, Long-Term Memory, Personalization, User Modeling, Workshop
National Category
Robotics Human Computer Interaction
Identifiers
URN: urn:nbn:se:kth:diva-333366DOI: 10.1145/3568294.3579956ISI: 001054975700207Scopus ID: 2-s2.0-85150414333OAI: oai:DiVA.org:kth-333366DiVA, id: diva2:1785071
Conference
18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023, Stockholm, Sweden, Mar 13 2023 - Mar 16 2023
Note

Part of ISBN 9781450399708

QC 20230801

Available from: 2023-08-01 Created: 2023-08-01 Last updated: 2023-10-16Bibliographically approved

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Irfan, Bahar

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