Pietro Stano
Papers
1
Total Citations
3
H-Index
1
About
Pietro Stano is a researcher at the forefront of autonomous vehicle systems and mobile robotics, with a focus on bridging the gap between simulation and real-world deployment. His work centers on modeling, positioning, and control of scaled robotic vehicles, particularly through the innovative application of deep reinforcement learning for path following. Stano’s major contribution lies in the design and experimental validation of control systems that enable scaled vehicles to accurately track trajectories—a critical component for advancing automated driving functions in indoor environments like warehousing and manufacturing. His most-cited paper (2024) demonstrates a complete pipeline from modeling to real-world testing, showcasing how reinforcement learning can outperform traditional controllers in precision and adaptability. With 3 citations already, this work is gaining traction as a practical benchmark for AD research. Stano’s achievements include integrating state-of-the-art positioning techniques with learning-based control, offering a cost-effective testbed for autonomous driving algorithms. His research is essential reading for students and engineers seeking to understand how scaled platforms can accelerate the development of safe, robust autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1