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Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World Learning
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.ORCID-id: 0000-0002-7983-079X
University of Naples Parthenope, Italy.
Boston Dynamics AI Institute, USA.
University of Cambridge, UK.
2024 (engelsk)Inngår i: HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2024, s. 1323-1325Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

The complex and largely unstructured nature of real-world situations makes it challenging for conventional closed-world robot learning solutions to adapt to such interaction dynamics. These challenges become particularly pronounced in long-term interactions where robots need to go beyond their past learning to continuously evolve with changing environment settings and personalize towards individual user behaviors. In contrast, open-world learning embraces the complexity and unpredictability of the real world, enabling robots to be “lifelong learners” that continuously acquire new knowledge and navigate novel challenges, making them more context-aware while intuitively engaging the users. Adopting the theme of “open-world learning”, the fourth edition of the “Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)”1 workshop seeks to bring together interdisciplinary perspectives on real-world applications in human-robot interaction (HRI), including education, rehabilitation, elderly care, service, and companionship. The goal of the workshop is to foster collaboration and understanding across diverse scientific communities through invited keynote presentations and in-depth discussions facilitated by contributed talks, a break-out session, and a debate.

sted, utgiver, år, opplag, sider
Association for Computing Machinery (ACM) , 2024. s. 1323-1325
Serie
ACM/IEEE International Conference on Human-Robot Interaction, ISSN 2167-2148
Emneord [en]
Adaptation, Continual Learning, Human-Robot Interaction, Lifelong Learning, Open-World Learning, Personalization, Workshop
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-344806DOI: 10.1145/3610978.3638159Scopus ID: 2-s2.0-85188103162OAI: oai:DiVA.org:kth-344806DiVA, id: diva2:1847612
Konferanse
19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024, Boulder, United States of America, Mar 11 2024 - Mar 15 2024
Merknad

QC 20240402

Part of ISBN 9798400703232

Tilgjengelig fra: 2024-03-28 Laget: 2024-03-28 Sist oppdatert: 2024-04-02bibliografisk kontrollert

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