Papers
6
Total Citations
176
H-Index
5
About
Dr. Luca Lonini is a leading researcher at the intersection of computational neuroscience, rehabilitation robotics, and sensorimotor control. His work primarily explores how humans learn and adapt to novel sensory feedback, with profound implications for advanced prosthetics and assistive technologies. A key contribution is demonstrating that humans can integrate augmented reality feedback into the sensorimotor control of a robotic hand (54 citations), a finding that challenges conventional approaches to prosthetic feedback. Dr. Lonini has also made significant strides in autonomous machine learning, developing algorithms for "intrinsically motivated learning" that enable robust active binocular vision (41 citations) and self-calibrating smooth pursuit (29 citations). These models, grounded in the efficient coding hypothesis, allow artificial systems to autonomously learn visual representations and motor commands. In rehabilitation, his pilot study on accelerometry-enabled measurement of walking performance with robotic exoskeletons (25 citations) has paved the way for more sophisticated, multi-feature clinical assessments. Through his work on generalization and interference in human motor control, Dr. Lonini continues to bridge the gap between neural computation and practical robotic applications, making him a pivotal figure in the future of human-machine interaction.
Research Focus
Key Achievements
Top Papers
- 1
- 2Robust active binocular vision through intrinsically motivated learning41 citations · 2013
- 3Self-calibrating smooth pursuit through active efficient coding29 citations · 2014
- 4
- 5Autonomous learning of active multi-scale binocular vision24 citations · 2013
- 6Generalization and Interference in Human Motor Control3 citations · 2013