The recent advancements in human-robot interaction (HRI) research have significantly focused on enhancing predictive capabilities and adaptive strategies to improve collaboration efficiency and safety. A notable trend is the development of multi-scale incremental modeling frameworks that capture intricate human motion dynamics across various temporal and spatial scales. These models, which often incorporate iterative refinement techniques, have shown substantial improvements in prediction accuracy and biomechanical consistency, particularly in scenarios requiring long-term forecasts. Additionally, there is a growing emphasis on personalized robot assistance, where robots infer human preferences and adapt their policies accordingly, leveraging motion prediction and utility inference modules to enhance user satisfaction and task efficiency. Ethical considerations and the integration of human factors into action recognition systems are also emerging as critical areas of focus, alongside the exploration of synthetic data generation to address dataset limitations. Furthermore, the incorporation of human motor control models into robot planning strategies is advancing co-manipulation techniques, enabling robots to generate human-like trajectories and adapt to varying conditions. The introduction of comprehensive datasets, such as TH"OR-MAGNI Act, is also playing a pivotal role in training predictive models for complex industrial environments. Overall, these developments are paving the way for more seamless, adaptive, and ethically sound human-robot interactions in diverse real-world applications.