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About

Anna Belardinelli is a distinguished researcher whose work spans computational models of visual attention, gaze-based intention estimation, and human-robot interaction (HRI). Her career trajectory reflects a fascinating evolution from foundational neuroscience-inspired models to cutting-edge applications in shared autonomy and AI-driven robotics. Her early contributions established biologically plausible frameworks for visual attention, including a TVA-based model integrating static and dynamic proto-objects to predict where humans look next (100 citations), and imitation-based learning of gaze shifts (30 citations). These works laid essential groundwork for understanding how machines can replicate human attentional behavior. Belardinelli's research then pioneered gaze-based intention estimation for human-robot collaboration, enabling robots to anticipate human goals during shared manipulation tasks — a body of work collectively amassing over 100 citations across multiple studies. Her more recent contributions showcase her versatility, incorporating Large Language Models into robotic planning and correction (CoPAL, 25 citations), explainable robot training via augmented reality (22 citations), and LLM-driven curiosity in HRI (7 citations). Her 2024 survey on gaze-based intention estimation (50 citations) already signals its importance as a field-defining reference. Across her career, Belardinelli has consistently bridged cognitive science, computer vision, and robotics to build more intuitive, human-aware autonomous systems.