Papers
6
Total Citations
127
H-Index
5
About
V. Schettino is a robotics researcher whose work spans human-robot interaction, teleoperation, and autonomous mobile systems. His most recognized contribution lies in the development of multimodal feedback systems for dual-arm robot teleoperation, particularly for demanding contact-driven tasks such as surface conditioning — wiping, polishing, and sanding — in dynamic industrial environments like automotive manufacturing. His 2020 paper combining haptic and visual feedback assistance garnered 74 citations, establishing him as a notable voice in teleoperation research, while a 2022 follow-up integrating inertial motion capture with haptics further refined remote dexterous manipulation with 21 citations. Beyond teleoperation, Schettino has made contributions to mobile robotics, including a fuzzy logic and CNN-based obstacle avoidance technique for human-robot collaborative warehouse environments and earlier work on vision-based position control and trajectory planning. His research also extends to assistive robotics, where he has explored shared control methodologies and learning-from-demonstration approaches for smart wheelchairs, aiming to enhance independence for users with limited motor abilities. Across these diverse threads, Schettino's work consistently addresses the challenge of enabling robots to operate safely and effectively alongside humans in complex, real-world settings.
Research Focus
Key Achievements
Top Papers
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