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About

Gabriele Meoni is a researcher at the intersection of assistive robotics and neuromorphic vision, whose work aims to empower individuals with motor disabilities and advance autonomous systems for space exploration. His primary contributions lie in developing human–machine interfaces for wheelchair-mounted robotic arms, where he leverages deep learning and computer vision to restore autonomy to users with upper limb impairments. Notably, his 2019 paper on a YOLOv2 convolutional neural network-based interface for assistive manipulators has garnered 14 citations, demonstrating its impact in the field. Meoni’s earlier work on position-based visual servoing and object detection using monocular cameras laid the groundwork for enabling robotic arms to interact with task objects in real time, addressing the critical challenge of translating complex robotic functionalities into accessible, user-friendly controls. More recently, Meoni has ventured into neuromorphic vision, co-authoring a 2023 study on generating synthetic event-based datasets for navigation and landing in space applications. This work capitalizes on the low power consumption and high dynamic range of event cameras, showcasing his versatility in applying cutting-edge sensing technologies to both terrestrial assistive systems and extraterrestrial autonomous navigation.