Caroline Rizzi

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About

Caroline Rizzi is a pioneering researcher at the intersection of affective computing, cognitive robotics, and autonomous decision-making. Her work is distinguished by a bold, biologically-inspired approach: she translates neuroscientific principles of fear learning into computational models that endow robots with situational awareness and adaptive behavior. Rizzi’s most significant contribution is the **Situation-Aware Fear Learning (SAFEL) model**, introduced in her 2016 paper (25 citations). SAFEL is a hybrid system that merges expert systems with brain-inspired fear mechanisms, enabling companion robots to predict and avoid undesirable or threatening situations—a critical step toward safer, more intuitive human-robot interaction. She further refined this model in a 2016 follow-up (4 citations), optimizing its predictive performance by analyzing parameter relationships. Her 2018 discussion paper (5 citations) extends SAFEL’s application to flexible decision-making in dynamic environments like RoboCup, showcasing its real-world relevance. By grounding robot learning in the brain’s own fear circuitry, Rizzi offers a compelling alternative to purely statistical approaches, paving the way for machines that not only compute but *anticipate* danger. Her work is essential reading for anyone interested in emotionally intelligent robotics.