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个人简介
My research interests include localization and mapping for mobile robotics, computer vision and ML applied to robot navigation, and human-robot interaction. I also have an interest in vision-based assistive technologies.
Research
Spatial AI: Situational awareness requires autonomous agents to build and maintain a multi-layered model of the environment, including both a geometric model (useful for navigation and coordination) and a semantic level (useful to execute high-level tasks and to provide more succinct information to human operators). I work on using human experiences to improve semantic understanding of the environment for mobile assistive robots.
Generative Visual Models and Intuitive Physics Understanding: My research tries to bring common sense understanding to robotic perception. Interacting with the environment requires to perceive objects and understand how actions influence their movement ad shape. Generative perception models can make sense of partial and noisy observations and reconstruct their shape and semantics. On the other hand, understanding the intuitive physics of objects interacting with each other will provide next-generation AI agents with a common sense knowledge base that will enable human-level interaction with a complex, dynamical environment.
Interpretable Sensor Fusion: Recent developments in machine learning have made possible to learn end-to-end motion estimation from visual, inertial and ranging devices. I study reasoned ways to learn sensor fusion strategies in deep VIO frameworks. At the same time, I study how to integrate novel sensor modalities such as millimeter wave radar and thermal imaging into a single framework.
Research
Spatial AI: Situational awareness requires autonomous agents to build and maintain a multi-layered model of the environment, including both a geometric model (useful for navigation and coordination) and a semantic level (useful to execute high-level tasks and to provide more succinct information to human operators). I work on using human experiences to improve semantic understanding of the environment for mobile assistive robots.
Generative Visual Models and Intuitive Physics Understanding: My research tries to bring common sense understanding to robotic perception. Interacting with the environment requires to perceive objects and understand how actions influence their movement ad shape. Generative perception models can make sense of partial and noisy observations and reconstruct their shape and semantics. On the other hand, understanding the intuitive physics of objects interacting with each other will provide next-generation AI agents with a common sense knowledge base that will enable human-level interaction with a complex, dynamical environment.
Interpretable Sensor Fusion: Recent developments in machine learning have made possible to learn end-to-end motion estimation from visual, inertial and ranging devices. I study reasoned ways to learn sensor fusion strategies in deep VIO frameworks. At the same time, I study how to integrate novel sensor modalities such as millimeter wave radar and thermal imaging into a single framework.
研究兴趣
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Stefano Rosa,Marco Randazzo, Ettore Landini,Stefano Bernagozzi, Giancarlo Sacco, Mara Piccinino,Lorenzo Natale
FRONTIERS IN ROBOTICS AND AI (2024): 1323675-1323675
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTHno. 5 (2021): 2593
semanticscholar(2021)
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