Back to ListAlessandra Sciutti is a Tenure Track Researcher and head of the CONTACT (COgNiTive Architecture for Collaborative Technologies) Unit of the Italian Institute of Technology (IIT). She received her B.S. and M.S. degrees in Bioengineering and her Ph.D. in Humanoid Technologies from the University of Genova in 2010. After two research periods in the USA and Japan, in 2018, she was awarded the ERC Starting Grant wHiSPER (www.whisperproject.eu), focused on the investigation of joint perception between humans and robots. She has published more than 100 papers and abstracts in international journals and conferences, coordinates the ERC POC Project ARIEL (Assessing Children Manipulation and Exploration Skills), and has participated in the coordination of the CODEFROR European IRSES project (https://www.codefror.eu/). She is currently Chief Editor of the HRI Section of Frontiers in Robotics and AI and Associate Editor for several journals, including the International Journal of Social Robotics, the IEEE Transactions on Cognitive and Developmental Systems, and Cognitive System Research. She is an ELLIS scholar and the corresponding co-chair of the IEEE RAS Technical Committee for Cognitive Robotics. Her research aims to investigate the sensory and motor mechanisms underlying mutual understanding in human-human and human-robot interaction. For more details on her research and the full list of publications, please check the Contact Unit website or her Google Scholar profile.
Wednesday 14th May 2025, 1:30 (CEST)
Alessandra Sciutti (Istituto Italiano di Tecnologia)
ABSTRACT
In this talk, I will present our research at the intersection of cognitive science and robotics, where robots are used both as experimental probes to study human cognition and as platforms to implement cognitive models that support intelligent behavior. By leveraging the paradigm of embodied cognition, we investigate how humans adapt their perception and actions during interaction with others—including robots—and how such mechanisms can be modeled computationally. For example, we have shown how mutual understanding can emerge through the reading of subtle body movements or gaze behavior, and how this can be used by robots to interpret and respond appropriately to human intentions. These findings inform the development of bio-inspired cognitive architectures that integrate sensorimotor information, internal motivation, and memory systems, enabling robots to adapt over time and across partners. Our work not only contributes to the scientific understanding of how humans perceive, predict, and collaborate, but also leads to the creation of autonomous, socially-aware machines capable of operating effectively in real-world, dynamic environments.
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