Recent advancements in human-robot interaction are focusing on enhancing personalization and adaptability, particularly in multi-user environments. New frameworks leverage large language models to create more engaging and contextually aware interactions, as seen in systems that manage long-term user profiles and dynamically adjust responses. Additionally, innovative approaches are emerging to facilitate intuitive communication, such as gloss-free sign language frameworks that streamline the interaction process by directly mapping gestures to commands, reducing reliance on complex annotations. Researchers are also exploring the impact of robot actions on human behavior, utilizing statistical methods to identify influential behaviors that can improve collaborative systems. Furthermore, gaze-based intent recognition is being refined to support users with limited motor capabilities, enhancing the efficiency of human-robot collaboration. These developments suggest a concerted effort to make robots more responsive and reliable, addressing commercial needs in sectors like healthcare and assistive technology, where effective communication and trust are paramount.