Lorenzo Peppoloni
Scuola Superiore Sant'Anna, Institute for Chemical and Physical Processes
Papers
9
Total Citations
264
H-Index
7
About
Lorenzo Peppoloni is a leading researcher at the intersection of human-robot interaction, augmented reality (AR), and wearable sensing. His work fundamentally advances how humans intuitively control and collaborate with robotic systems, particularly in industrial and assistive contexts. Peppoloni’s most impactful contribution is demonstrating that AR significantly improves teleoperation performance in industrial assembly tasks, with his 2017 paper on robotic embodiment garnering 84 citations. He pioneered the development of immersive, ROS-integrated frameworks that allow operators to control robots using natural hand motions and muscle contractions, as seen in his highly cited 2015 work (72 citations). A key technical achievement is his novel 7-degree-of-freedom model for upper limb kinematic reconstruction using wearable inertial sensors (46 citations), which enables precise motion tracking without external cameras. Peppoloni has also advanced probabilistic graphical models for motion tracking and applied stacked generalization to scene analysis. His research extends to assistive robotics, where he develops methods for evaluating elderly users’ skills to enable proactive, user-centered robotic assistance. Through his integration of AR, wearable sensors, and ROS, Peppoloni has created practical pathways for more natural, efficient, and accessible human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1
- 2Immersive ROS-integrated framework for robot teleoperation72 citations · 2015
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- 6Stacked generalization for scene analysis and object recognition12 citations · 2014
- 7Towards Skills Evaluation of Elderly for Human-Robot Interaction8 citations · 2018
- 8
- 9Programming of a Mobile Robotic Manipulator through Demonstration2 citations · 2012