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Irfan, B., Staffa, M., Bobu, A. & Churamani, N. (2024). Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World Learning. In: HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction: . Paper presented at 19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024, Boulder, United States of America, Mar 11 2024 - Mar 15 2024 (pp. 1323-1325). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World Learning
2024 (English)In: HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2024, p. 1323-1325Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2024
Series
ACM/IEEE International Conference on Human-Robot Interaction, ISSN 2167-2148
Keywords
Adaptation, Continual Learning, Human-Robot Interaction, Lifelong Learning, Open-World Learning, Personalization, Workshop
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:kth:diva-344806 (URN)10.1145/3610978.3638159 (DOI)2-s2.0-85188103162 (Scopus ID)
Conference
19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024, Boulder, United States of America, Mar 11 2024 - Mar 15 2024
Note

QC 20240402

Part of ISBN 9798400703232

Available from: 2024-03-28 Created: 2024-03-28 Last updated: 2024-04-02Bibliographically approved
McMillan, D., Jaber, R., Cowan, B. R., Fischer, J. E., Irfan, B., Cumbal, R., . . . Lee, M. (2023). Human-Robot Conversational Interaction (HRCI). In: HRI 2023: Companion of the ACM/IEEE International Conference on Human-Robot Interaction. Paper presented at 18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023, Stockholm, Sweden, Mar 13 2023 - Mar 16 2023 (pp. 923-925). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Human-Robot Conversational Interaction (HRCI)
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2023 (English)In: HRI 2023: Companion of the ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, p. 923-925Conference paper, Published paper (Refereed)
Abstract [en]

Conversation is one of the primary methods of interaction between humans and robots. It provides a natural way of communication with the robot, thereby reducing the obstacles that can be faced through other interfaces (e.g., text or touch) that may cause difficulties to certain populations, such as the elderly or those with disabilities, promoting inclusivity in Human-Robot Interaction (HRI).Work in HRI has contributed significantly to the design, understanding and evaluation of human-robot conversational interactions. Concurrently, the Conversational User Interfaces (CUI) community has developed with similar aims, though with a wider focus on conversational interactions across a range of devices and platforms. This workshop aims to bring together the CUI and HRI communities to outline key shared opportunities and challenges in developing conversational interactions with robots, resulting in collaborative publications targeted at the CUI 2023 provocations track.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023
Keywords
Conversational User Interaction, Embodied Interaction, HRI
National Category
Human Computer Interaction Robotics
Identifiers
urn:nbn:se:kth:diva-333370 (URN)10.1145/3568294.3579954 (DOI)001054975700205 ()2-s2.0-85150450577 (Scopus ID)
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
Irfan, B., Ramachandran, A., Staffa, M. & Gunes, H. (2023). Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Adaptivity for All. In: HRI 2023: Companion of the ACM/IEEE International Conference on Human-Robot Interaction. Paper presented at 18th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2023, Stockholm, Sweden, Mar 13 2023 - Mar 16 2023 (pp. 929-931). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Adaptivity for All
2023 (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
Keywords
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:nbn:se:kth:diva-333366 (URN)10.1145/3568294.3579956 (DOI)001054975700207 ()2-s2.0-85150414333 (Scopus ID)
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
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-7983-079X

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