Laura Marazzato

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About

Laura Marazzato’s research lies at the intersection of bio-inspired robotics, sensory-motor control, and humanoid vision systems. Her work focuses on translating neural mechanisms from the human vestibular and oculomotor systems into robust, adaptive algorithms for robots. In her most-cited study (22 citations), she compared two bio-inspired adaptive models of the Vestibulo-Ocular Reflex (VOR) implemented on the iCub humanoid robot, demonstrating how biological principles can stabilize robot vision during head motion. Her second major contribution (14 citations) involved implementing a visual tracking model that mimics the human combination of smooth pursuit and saccadic eye movements, enabling the iCub to predict and follow moving targets with human-like behavior. These contributions are foundational for developing robots that can interact more naturally and reliably in dynamic environments. Marazzato’s work is notable for bridging computational neuroscience and robotics, offering practical pathways to endow machines with adaptive, biologically plausible perception. Her research continues to influence the design of autonomous systems requiring stable, predictive visual tracking and sensorimotor coordination.