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About

Alberto Bacchin is a robotics researcher whose work focuses on human-robot interaction, autonomous navigation, and manipulation in unstructured environments. His key research areas include people-aware navigation for telepresence robots, semantic grasping with deep learning, and AI-driven waste sorting. Bacchin’s major contributions include developing **Preference-Based People-Aware Navigation** (2024, 6 citations), which enables telepresence robots to navigate crowded spaces by estimating people’s willingness to interact, fusing context and social cues into shared intelligence. He also created **FSG-Net** (2023, 4 citations), a few-shot learning model for semantic robot grasping that selects objects intelligently without massive datasets, and **WasteGAN** (2024, 3 citations), a generative adversarial network that augments data for robotic waste sorting on cluttered conveyor belts. Additionally, his work on **People Tracking in Panoramic Video** (2023, 3 citations) guides robots using wide-angle visual tracking. Bacchin’s research is notable for tackling real-world challenges—social navigation, efficient grasping, and sustainability—with innovative AI solutions that reduce data dependency. His work has accumulated over 16 citations, reflecting growing impact in robotics and computer vision.