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  • 1.
    Irfan, Bahar
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Kuoppamäki, Sanna
    KTH, Skolan för kemi, bioteknologi och hälsa (CBH), Medicinteknik och hälsosystem, Hälsoinformatik och logistik.
    Skantze, Gabriel
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Recommendations for designing conversational companion robots with older adults through foundation models2024Ingår i: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 11, artikel-id 1363713Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Companion robots are aimed to mitigate loneliness and social isolation among older adults by providing social and emotional support in their everyday lives. However, older adults’ expectations of conversational companionship might substantially differ from what current technologies can achieve, as well as from other age groups like young adults. Thus, it is crucial to involve older adults in the development of conversational companion robots to ensure that these devices align with their unique expectations and experiences. The recent advancement in foundation models, such as large language models, has taken a significant stride toward fulfilling those expectations, in contrast to the prior literature that relied on humans controlling robots (i.e., Wizard of Oz) or limited rule-based architectures that are not feasible to apply in the daily lives of older adults. Consequently, we conducted a participatory design (co-design) study with 28 older adults, demonstrating a companion robot using a large language model (LLM), and design scenarios that represent situations from everyday life. The thematic analysis of the discussions around these scenarios shows that older adults expect a conversational companion robot to engage in conversation actively in isolation and passively in social settings, remember previous conversations and personalize, protect privacy and provide control over learned data, give information and daily reminders, foster social skills and connections, and express empathy and emotions. Based on these findings, this article provides actionable recommendations for designing conversational companion robots for older adults with foundation models, such as LLMs and vision-language models, which can also be applied to conversational robots in other domains.

  • 2.
    Irfan, Bahar
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Ramachandran, Aditi
    Van Robotics, USA.
    Staffa, Mariacarla
    University of Naples Parthenope, Italy.
    Gunes, Hatice
    University of Cambridge, UK.
    Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Adaptivity for All2023Ingår i: HRI 2023: Companion of the ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, s. 929-931Konferensbidrag (Refereegranskat)
    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.

  • 3.
    Irfan, Bahar
    et al.
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Staffa, Mariacarla
    University of Naples Parthenope, Italy.
    Bobu, Andreea
    Boston Dynamics AI Institute, USA.
    Churamani, Nikhil
    University of Cambridge, UK.
    Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Open-World Learning2024Ingår i: HRI 2024 Companion - Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2024, s. 1323-1325Konferensbidrag (Refereegranskat)
    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.

  • 4.
    McMillan, Donald
    et al.
    Stockholm University, Stockholm, Sweden.
    Jaber, Razan
    University College Dublin, Dublin, Ireland.
    Cowan, Benjamin R.
    University College Dublin, Dublin, Ireland.
    Fischer, Joel E.
    University of Nottingham, Nottingham, United Kingdom.
    Irfan, Bahar
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Cumbal, Ronald
    KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Tal, musik och hörsel, TMH.
    Zargham, Nima
    Digital Media Lab, University of Bremen, Germany.
    Lee, Minha
    Eindhoven University of Technology, Eindhoven, The Netherlands.
    Human-Robot Conversational Interaction (HRCI)2023Ingår i: HRI 2023: Companion of the ACM/IEEE International Conference on Human-Robot Interaction, Association for Computing Machinery (ACM) , 2023, s. 923-925Konferensbidrag (Refereegranskat)
    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.

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