| Titel | BA | SA | MA | HW | exp | the | kon | Eintrag |
|---|---|---|---|---|---|---|---|---|
| Agile Trajectory Planning in Competitive Multi-Agent Scenarios | 2026-03-20 |
| mattia.piccinini@tum.de | |
| Room | Hochbrück 3.4.18 |
| Website | mattiapiccinini95.github.io |
| ORCID | ORCID |
| Google Scholar | Google Scholar Profil |
| Researchgate | Researchgate Profil |
Research Topics
- Global, local, and behavioral trajectory planning
- Model predictive control
- Neural networks with physics-encoded architectures
- World foundation models
- Motion control
- Adaptive planning and control
- State estimation
- Mobile ground robotics
Vita
Mattia Piccinini is a Humboldt Postdoctoral Fellow at the Autonomous Vehicle Systems (AVS) Lab, Technical University of Munich (TUM). He received a B.Sc. in Industrial Engineering (2017, cum laude) and an M.Sc. in Mechatronics Engineering (2019, cum laude) from the University of Trento, Italy, where he also earned a Ph.D. in Autonomous Systems (2024, cum laude) under the supervision of Prof. Francesco Biral. His doctoral thesis, “Artificial Drivers for Online Time-Optimal Vehicle Trajectory Planning and Control,” was awarded the 2025 IEEE ITSS Best Dissertation Award.
He is the recipient of the TUM Global Postdoctoral Fellowship (2024) and the Humboldt Postdoctoral Fellowship (2025), supporting his research at TUM-AVS since December 2024. He has held visiting research positions at the Universität der Bundeswehr Munich (2022) and Eindhoven University of Technology (2025). He serves as an Associate Editor for the IEEE IROS and IEEE ITSC conferences.
His research focuses on physics-guided motion generation, control, and estimation for mobile ground robots operating in uncertain and dynamic environments.