Irene Di Giulio
Papers
1
Total Citations
9
H-Index
1
About
Irene Di Giulio is a leading researcher in social robotics and human-robot interaction, with a focus on enabling robots to learn from and replicate the nuanced nonverbal cues that define human communication. Her key research areas include multimodal machine learning, imitation learning, and the computational modeling of social behaviors. Di Giulio’s most notable contribution is the creation of a pioneering multimodal dataset for robot learning to imitate social human-human interaction (2023), which has already garnered 9 citations and serves as a foundational resource for developing more intuitive and responsive robotic systems. By capturing and analyzing the complex interplay of gestures, gaze, and posture in human exchanges, her work bridges the gap between raw sensory data and socially aware robotic actions. This achievement not only advances the field of autonomous social agents but also holds promise for applications in assistive technology and collaborative robotics. Di Giulio’s research is distinguished by its rigorous integration of behavioral science and engineering, making her a key figure in the quest to build machines that can genuinely understand and engage in human social dynamics.
Research Focus
Key Achievements
Top Papers
- 1