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Abstract: Vision is an exploratory behaviour that relies heavily on the dynamic relationship between actions and sensory feedback.

For any agent—whether animal or robotic—processing visual sensory input efficiently is crucial for understanding and interacting with its environment. The key challenge lies in selectively filtering relevant information from the constant stream of complex sensory data. This process, known as selective attention, is also driven by the intricate interplay between bottom-up and top-down mechanisms, which together organize and interpret visual scenes.

I will explore how biologically plausible models for visual attention can enhance robotic interaction with the environment trying to understand the role of neuromorphic hardware in facilitating active vision and its limitations.

Bio: Giulia D'Angelo is currently an MSCA Postdoctoral Fellow at the Czech Technical University in Prague, focusing on neuromorphic algorithms for active vision. She obtained a Bachelor's degree in Biomedical Engineering at The University of Genoa and a Master's degree in Neuroengineering, during which she developed a neuromorphic system for the egocentric representation of peripersonal visual space at King's College London. She earned her PhD in neuromorphic algorithms at the University of Manchester, in collaboration with the Event-Driven Perception for Robotics Laboratory at the Italian Institute of Technology, where she proposed a biologically plausible model for event-driven, saliency-based visual attention.

She was recently awarded the Marie Skłodowska-Curie Fellowship, through which she explores sensorimotor contingency theories for neuromorphic active vision algorithms.

By: Aleks Kossowska
Last updated: Tuesday, 12 November 2024