Simone Lugani
Papers
2
Total Citations
4
H-Index
1
About
Simone Lugani is a researcher at the forefront of efficient computer vision, specializing in affordance segmentation for wearable robotic systems. His work addresses the critical challenge of enabling robots to understand object interaction possibilities—such as where to grasp or step—directly on embedded, resource-constrained devices. Lugani’s key contributions lie in designing lightweight neural network architectures that maintain high accuracy while operating under severe computational and memory limits. His 2024 paper, "Lightweight Neural Networks for Affordance Segmentation," has already garnered 3 citations for its innovative enhancement of the decoder module, a vital step for real-time performance. Building on this, his 2025 work pioneers the use of RGB-D cameras in this domain, proposing a novel hardware-aware neural architecture search space that fills the Pareto-optimal front—balancing speed, power, and precision. This approach directly tackles the underexplored potential of depth sensors in wearable robotics, paving the way for more intuitive and responsive assistive devices. Lugani’s research is a compelling blend of algorithmic elegance and practical engineering, offering a clear path toward deploying sophisticated visual intelligence on the smallest of platforms.
Research Focus
Key Achievements
Top Papers
- 1
- 2