Lorenzo Landolfi
Papers
4
Total Citations
25
H-Index
3
About
Lorenzo Landolfi’s research lies at the intersection of robotics, human-robot interaction, and intelligent systems, with a focus on making autonomous machines more perceptive, adaptive, and usable in real-world settings. His work spans three key areas: tele-ultrasonography interfaces, multi-camera calibration for outdoor tracking, and working memory architectures for human-aware industrial robotics. In his most cited paper (9 citations), Landolfi evaluated a virtual reality-based tele-ultrasonography system that integrates haptic feedback and 2D/3D visualization to enhance remote diagnosis—a contribution with clear implications for telemedicine. He also tackled the foundational problem of multi-camera extrinsic calibration for real-time tracking in large outdoor environments (8 citations), addressing a critical need for IoT and edge computing applications. More recently, Landolfi has advanced human-aware navigation in industrial settings by implementing two working memory architectures—a GRU-based model and a bioinspired alternative called WorkMATe—on an RB-KAIROS+ mobile manipulator. His user-centered approach to training these systems, detailed in his 2023 papers (5 and 3 citations), demonstrates a commitment to designing robots that can anticipate and adapt to human behavior, paving the way for safer, more efficient human-robot collaboration in factories.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4