Papers
44
Total Citations
583
H-Index
13
About
Stefano Rosa is a robotics and autonomous systems researcher whose work spans deep reinforcement learning, multi-sensor state estimation, cloud robotics, and human-robot interaction. His most influential contributions address fundamental challenges in robot perception and autonomy: his 2018 paper on training-wheel-assisted deep reinforcement learning (83 citations) introduced an elegant approach to accelerating DRL training for robotic applications, while his 2020 work DeepTIO (82 citations) tackled the critical problem of reliable odometry in visually-degraded environments by fusing thermal imaging with inertial sensing. His 2022 research on selective sensor fusion further advanced robust state estimation for autonomous vehicles and mobile robots. Beyond perception, Rosa has made meaningful contributions to socially impactful robotics. His PARLOMA system pioneered remote tactile sign language communication for deaf-blind individuals, and his cloud robotics platforms have enabled museum telepresence for mobility-impaired users and autonomous UAV-based smart city monitoring. More recently, his 5G-enabled tour guide robot demonstrates his sustained commitment to deploying intelligent systems in real-world environments. With over 300 cumulative citations, Rosa's body of work reflects a researcher equally at home advancing core autonomy algorithms and translating robotic technology into solutions with genuine societal benefit.
Research Focus
Key Achievements
Top Papers
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- 2DeepTIO: A Deep Thermal-Inertial Odometry With Visual Hallucination82 citations · 2020
- 3Learning Selective Sensor Fusion for State Estimation36 citations · 2022
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- 8Fly4SmartCity: A cloud robotics service for smart city applications22 citations · 2016
- 9Tour guide robot: a 5G-enabled robot museum guide21 citations · 2024
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