Luca Benini
Papers
38
Total Citations
812
H-Index
15
About
Luca Benini is a pioneering researcher at the intersection of embedded artificial intelligence, autonomous robotics, and human-machine interaction, whose work has fundamentally shaped how deep learning is deployed on resource-constrained edge devices. His most celebrated contributions center on enabling sophisticated AI-driven visual navigation on nano-scale drones — platforms once deemed too power-limited for onboard intelligence. His landmark 2019 paper on DNN-based visual navigation engines for autonomous nano-drones (185 citations) demonstrated that sub-10-watt platforms could execute real-time deep neural network inference, a breakthrough that redefined expectations for IoT edge computing. Benini has equally advanced human-robot interfacing, developing machine-learning approaches to decode surface electromyographic signals for intuitive robotic hand control (115 citations), and more recently pioneering ultra-low-power transformer architectures for gesture recognition. His hardware contributions are equally significant, including a highly efficient AI-IoT System-on-Chip achieving 12.4 TOPS/W, and NanoSLAM, bringing full onboard simultaneous localization and mapping to tiny robots. Spanning prosthetics, e-skin sensing, and cognitive human-robot interaction, Benini's body of work reflects a rare ability to bridge fundamental algorithmic innovation with practical, energy-conscious hardware design — making him an essential reference for any researcher working at the edge of embedded intelligence.
Research Focus
Key Achievements
Top Papers
- 1A 64-mW DNN-Based Visual Navigation Engine for Autonomous Nano-Drones185 citations · 2019
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- 8Human–Robot Cognitive Interaction28 citations · 2008
- 9NanoSLAM: Enabling Fully Onboard SLAM for Tiny Robots26 citations · 2023
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