Antonio Di Tecco

Papers

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About

Antonio Di Tecco is a researcher focused on advancing human-robot interaction (HRI) and teleoperation systems, with a particular emphasis on improving dexterity, user awareness, and task effectiveness. His major contributions center on the design and evaluation of virtual dashboards for teleoperated grasping tasks, addressing the critical challenge of coupling stereoscopic vision and force/tactile feedback to enhance object manipulation. His 2024 work on the "Virtual Dashboard Design for Grasping Operations in Teleoperation Systems" (5 citations) introduces a subsystem tested within the Sully teleoperation platform, demonstrating tangible improvements in grasp precision. Di Tecco’s 2025 follow-up study (3 citations) systematically investigates how such dashboards impact task quality and user awareness in pick-and-place operations. More recently, he has pioneered the use of machine learning to predict user satisfaction in HRI teleoperation tasks, introducing the multimodal Robot Motion Dataset (RMD) for autonomous ground vehicles (1 citation). His work bridges hardware-software integration and user-centered design, offering practical tools for more intuitive and effective teleoperation systems. Di Tecco’s research is highly relevant for students and engineers working on assistive robotics, remote manipulation, and adaptive HRI interfaces.