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About

Caterina Ceccato investigates the intersection of human-robot interaction (HRI), adaptive robotics, and brain-computer interfaces (BCI) to create more responsive, personalized learning and assistive technologies. Her work centers on developing systems that can monitor and respond to a user’s cognitive state in real time. A key contribution is her passive BCI system for monitoring engagement during robot-assisted language learning, which uses EEG signals to adapt pedagogical strategies for a personalized experience (12 citations). She further demonstrated that embodied interaction with an adaptive robot tutor significantly boosts user engagement and learning performance (13 citations). Ceccato has also explored the frontier of social intelligence in HRI, comparing Wizard-of-Oz methods with GPT-4-driven autonomy for brainstorming tasks (9 citations), and has developed a BCI-controlled robot assistant for navigation and object manipulation in virtual smart home environments (5 citations). Her research is notable for bridging neurotechnology and robotics to create systems that not only assist but also adapt to human attention and intention, paving the way for more intuitive and effective human-machine collaboration in education and daily living.