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.