Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Adaptivity for All

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 in-depth 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.