Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI): Overcoming Inequalities with Adaptation

Global inequalities in access to essential resources such as education, healthcare, and technology continue to widen social and economic disparities, especially in underserved and underrepresented communities. The growing integration of foundation models and other machine learning systems in robots offers promising and personalized solutions that can adapt to various individuals, situations, and environments, potentially addressing some of these gaps. By learning from interactions and evolving with local conditions, these systems can provide individualized support, such as assisting older adults with daily tasks, aiding children with special needs in learning environments, or empowering people with disabilities to live more independently. Building trust and fostering collaboration between humans and robots will help ensure that these systems meet the unique needs of all individuals, especially within long-term human-robot interaction (HRI). With this year's theme of 'Overcoming Inequalities with Adaptation', in line with the overall theme of the conference 'Robots for a Sustainable World', the fifth edition of the 'Lifelong Learning and Personalization in Long-Term Human-Robot Interaction (LEAP-HRI)'l workshop aims to bring together insights across diverse disciplines, exploring how continually evolving robots can effectively operate in diverse environments, promoting greater equity, inclusivity, and empowerment for individuals and communities. The workshop aims to facilitate collaborations across diverse scientific perspectives through a keynote presentation, panel discussions, and in-depth discussions on the contributed talks, attempting to shape a more sustainable and equitable future through adaptive advancements in long-term HRI.