The Flagship’s transversal projects are designed to foster deep collaboration across laboratories, disciplines, and methodological approaches. Rather than advancing isolated research units, these projects bring together two or more labs to address shared scientific challenges from complementary perspectives.
Spanning neuroscience, biology, robotics, and artificial intelligence, the transversal projects explore how behavior, cognition, and interaction emerge across living and artificial systems. They combine experimental work in humans and animal models with computational approaches and embodied robotic systems, enabling insights to move fluidly across scales—from neural mechanisms to behavior, and from biological systems to adaptive machines.
A defining feature of the transversal projects is their emphasis on integration. Each project links distinct expertise, data types, and conceptual frameworks, creating a common ground where theories can be tested across domains and results can inform both fundamental understanding and applied technologies. This approach supports the identification of shared principles underlying learning, social interaction, motor control, and adaptation, while also highlighting meaningful differences between systems.
By design, the transversal projects aim to generate impact beyond individual disciplines. Their outcomes are expected to inform future developments in healthcare and rehabilitation, contribute to more natural and trustworthy human–robot interaction, and support the advancement of personalized and socially aware artificial intelligence. At the same time, they serve as a catalyst for building a cohesive research community within the Flagship, strengthening long‑term collaboration and knowledge exchange across labs.
4R: 4aging Rules in Roots, Rodents, and Robots
Barbara Mazzolai, Peter Neri, Luca Pandolfini, Raffaella Tonini
This project investigates how different living systems—from plants to animals—balance exploration and exploitation during foraging, and how these principles can guide adaptive control strategies in robotics.
Sync&Heal: Collaborative Robotics for Post Stroke Rehabilitation
Lorenzo Natale, Luciano Fadiga
Sync&Heal explores how socially interactive humanoid robots can support post stroke rehabilitation through synchronized and collaborative motor tasks, enhancing patient engagement and recovery.
fMRI and Chloride Transporters in Models of Autism
Laura Cancedda, Alessandro Gozzi
This project integrates neuroimaging and molecular approaches to identify biologically meaningful subtypes of autism, with the goal of supporting more personalized and targeted therapeutic strategies.
Synaptic Organization and Epilepsy: A Computational Perspective
Andrea Barberis, Peter Neri
By combining advanced imaging with computational modeling, this project examines how disruptions in synaptic organization contribute to abnormal neural activity in epilepsy.
Micromovement with Neuromorphic Sensing
Alessandro D’Ausilio, Luciano Fadiga, Chiara Bartolozzi
This project develops neuromorphic sensing and machine learning tools to capture subtle features of human movement, enabling more natural human–robot interaction and refined motor behavior analysis.
Cognitive Load in Human–Robot Collaboration
Arash Ajoudani, Agnieszka Wykowska
Focusing on collaborative assembly tasks, this project studies behavioral and neural markers of cognitive load to design adaptive robotic systems that improve performance, safety, and user experience.
Interneural Synchrony in Emotion Recognition
Giacomo Novembre, Francesco Papaleo
This cross species project investigates how synchronized brain activity between individuals supports emotion recognition, linking social cognition mechanisms in humans and animal models.
Modeling Multimodal Goal Representation for Child–Robot Learning
Monica Gori, Alessandra Sciutti
This project explores how humans—particularly children—use language and visual representations to express goals, and how robots can learn from these signals to support mutual learning, inclusion, and social development.