Author

Edoardo Ghignone

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Preprint Oct 2026

MAP2: Model- and Acceleration-Based Pursuit with MPC and Gaussian Process Residual Learning for Autonomous Racing

Autonomous racing requires accurate trajectory tracking near the handling limits of a vehicle while maintaining low computational latency. Geometric controllers are computationally efficient, but the Ackermann steering geometry becomes invalid under limit-handling conditions. Model- and Acceleration-based Pursuit (MAP)...

Wei-Zhang Huang, Cheng Hu, Edoardo Ghignone et al. · 0 citations

Jul 2026

Continual-RL for Generalization in Autonomous Racing on the RoboRacer Platform

This work tries to address the gap in Continual Reinforcement Learning by proposing a continual RL framework based on Continual Backpropagation that is able, with only real-world data, to train a generalistic policy on a set of tracks and then fine- tune it within 15 minutes to outperform classical controllers.

Joel Siegert, Edoardo Ghignone, Michele Magno · arXiv.org · 0 citations