Ignazio Olivieri
Papers
1
Total Citations
3
H-Index
1
About
Ignazio Olivieri is a robotics researcher whose work centers on the modeling, control, and experimental validation of autonomous mobile systems. His primary contributions lie at the intersection of vehicle dynamics, positioning, and deep reinforcement learning, with a strong emphasis on bridging the gap between simulation and real-world deployment. Olivieri’s most cited work, "Modeling, Positioning, and Deep Reinforcement Learning Path Following Control of Scaled Robotic Vehicles," introduces a novel framework for path following in scaled robotic platforms, demonstrating how reinforcement learning can be effectively applied to steering control for automated driving functions. This research is particularly significant for its experimental validation, offering a practical test bench for evaluating autonomous driving algorithms in controlled environments. By focusing on scaled vehicles, Olivieri enables cost-effective, repeatable testing that accelerates the development of full-scale AD systems. His work has garnered attention within the robotics and autonomous systems community, with his top paper accumulating 3 citations since 2024, reflecting its emerging impact. Olivieri’s achievements include advancing the integration of deep RL into real-time vehicle control, a critical step toward more adaptive and robust autonomous navigation.
Research Focus
Key Achievements
Top Papers
- 1