Organizers

Short Bio: I received my Master’s degree in Automation Engineering from Politecnico di Bari, IT, in 2017, and my PhD in Computer Science from University of Verona in 2021. I am currently a research fellow and assistant professor in Artificial Intelligence and Robotics at University of Verona. My research is mainly focused on robotics and AI integration. In particular, during my PhD I worked on symbolic reconfigurable planning for safety-critic surgical robotic systems, integrating task and motion planning. As a post-doctoral researcher, I am now focusing on the integration of data-driven and symbolic (neurosymbolic) AI for autonomous agent planning. This involves the combination of reinforcement learning, non-monotonic reasoning and inductive logic programming for explainability, trustability and higher-efficiency of autonomous agents. At the same time, I am recently working on the problem of explainable system analysis exploiting causal discovery techniques, which can be ultimately combined into reinforcement learning frameworks for explainability or anomaly detection purposes. I have served as reviewer and PC member for several national and international conferences and journals, including: IEEE Transactions on Medical Robotics and Bionics; IEEE Transactions on Control System Technologies; IEEE Robotics and Automation Magazine; ACM AAMAS, ECAI, ICAPS Conferences; IEEE ICRA, IROS, CASE, Humanoids Conferences; AIxIA thematic workshops.

Short Bio: I am currently a Postdoctoral Research Fellow at the University of Verona (Italy), where my research focuses on nonlinear control, optimization, planning, and reinforcement learning for autonomous systems. I received my PhD in Computer Science in May 2025 from the University of Verona, under the supervision of Prof. Riccardo Muradore and Prof. Alessandro Farinelli. My dissertation, “Stochastic Constrained Optimal Control and Planning via Monte Carlo Tree Search for Autonomous Aircraft Systems,” contributed novel methodologies for addressing uncertainty in optimal control and planning of autonomous aerial vehicles. During my doctoral studies, I undertook a research visit at The Ohio State University under the guidance of Prof. Andrea Serrani, where I investigated geometric approaches for adaptive control of uncertain linear systems. My research has led to publications in top venues, including Robotics and Autonomous Systems, IEEE Robotics and Automation Letters, and major international conferences such as IROS, ACC, ECC, and IAS. I have also contributed to the scientific community as a reviewer for top journals and conferences, and I am an active member of IEEE, particularly within the Control Systems Society and the Robotics and Automation Society. Overall, my academic career is driven by the goal of developing advanced control, planning, and learning techniques that bridge theory and application, with particular emphasis on safety, robustness, and autonomy in complex dynamical systems.

Short Bio: Alessandro Farinelli is full professor at University of Verona, Department of Computer Science. His research interests focus on developing novel methodologies for Artificial Intelligence systems applied to robotics and cyber physical systems. In particular, he focuses on multi-agent coordination, decentralized optimization, reinforcement learning and data analysis for cyber-phisical systems.  Alessandro Farinelli was principal investigator for several national and international research projects in the broad area of Artificial Intelligence. His research contributions target mainly international journals in the area of Artificial Intelligence (e.g., Artificial Intelligence journal and Journal of Artificial Intelligence Research) and Autonomous Robotic Systems (Autonomous Robots and Robotics and Autonomous Systems). The main scientific conferences he contributes to (both as organizer and speaker) include the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), the International Joint Conference on Artificial Intelligence (IJCAI) and the International Conference on Intelligent Robots and Systems (IROS). 

Short Bio: Mattia Piccinini is a Humboldt Post-doctoral Fellow at the Professorship of Autonomous Vehicle Systems (AVS) at the Technical University of Munich (TUM). He received an M.Sc. in mechatronics engineering (cum laude) and a Ph.D. (cum laude) in autonomous systems from the University of Trento, Italy, in 2019 and 2024 respectively. He obtained the ITSS Best Dissertation Award (2025), the TUM Global Post-doctoral Fellowship (2024), and the Humboldt Post-doctoral Fellowship (2025). He spent visiting research periods at the University of the Bundeswehr (Germany) and at the Eindhoven University of Technology (The Netherlands). His research focuses on physics-guided motion generation and control of mobile ground robots. He serves as Associate Editor for IEEE ITSC and IEEE IROS. 

Short Bio: Johannes Betz is an Assistant Professor in the Department of Mobility Systems Engineering at the Technical University of Munich (TUM), where he is leading the Autonomous Vehicle Systems (AVS) lab. He is one of the founders of the TUM Autonomous Motorsport team. His research focuses on developing adaptive dynamic path planning and control algorithms, decision-making algorithms that work under high uncertainty in multi-agent environments, and validating the algorithms on real-world robotic systems. He earned a B.Eng. (2011) from the University of Applied Science Coburg, an M.Sc. (2012) from the University of Bayreuth, an M.A. (2021) in philosophy from TUM, and a Ph.D. (2019) from TUM.