Modeling perception of human-robot interaction: toward natural and social HRI experiences

The increasing integration of robotic systems into various domains such as industrial, medical, and social environments highlights the growing importance of user-centered design to ensure these technologies enhance human well-being and inclusivity. This thesis investigates the development of a generalized human-driven control architecture aimed at enabling natural and socially-aware human-robot interaction. The proposed framework draws on insights from biomechanics, physiology, psychology, and social science to construct a comprehensive model of the user’s experience. This model enables robotic systems to dynamically adapt their behavior, fostering interactions that are both personalized and empathetic. The research focuses on two domains where user experience is central: collaborative industrial robotics and robotic neurorehabilitation. In industrial settings, cobots are designed to complement human capabilities, improving ergonomics, efficiency, and operator well-being. In neurorehabilitation, robotic systems aim to enhance therapeutic practices and patient engagement through socially-responsive behaviors. These two application areas serve as testbeds for implementing and validating the proposed framework. The methodology consists of three phases: a thorough literature review to identify key user experience factors in HRI; the design and implementation of experimental setups in the selected domains; and validation through empirical studies with diverse participant groups, including neurodivergent individuals. The approach emphasizes ethical considerations and non-invasive data collection, ensuring usability and privacy compliance. Experimental results demonstrate the framework’s capacity to integrate heterogeneous data—biomechanical, physiological, psychological, and social—into actionable insights for real-time robotic adaptation. By enabling smoother, more inclusive interactions, the framework supports a user-centered approach to robotics. Based on these results, the thesis outlines practical guidelines for replicating and extending the architecture across different scenarios, emphasizing its potential to improve usability and social acceptability in HRI. The findings underscore the relevance of interdisciplinary approaches in designing robotic systems that prioritize human experience. Future work will focus on refining adaptability and extending applications to broader contexts.