Gioele Zardini
Gioele is a Postdoctoral Scholar in the Department of Aeronautics and Astronautics at Stanford University, and an incoming faculty at MIT in Fall 2024. He received his BSc., MSc., and Ph.D. in Mechanical Engineering with focus in Robotics, Systems and Control from ETH Zurich in 2017, 2019, and 2023 respectively. He spent time in Singapore as a researcher at nuTonomy (then Aptiv, now Motional), at Stanford University (working with Marco Pavone) and at MIT (in 2020 working with David Spivak, and in 2023 with Munther Dahleh).
Driven by societal challenges, the goal of his research is to develop efficient computational tools and algorithmic approaches to formulate and solve complex, interconnected system design and autonomous decision making problems. His research interests include the co-design of sociotechnical systems, compositionality in engineering, applied category theory, decision and control, optimization, and game theory, with applications to intelligent transportation systems, autonomy, and complex networks and infrastructures.
He is the creator of Autonomy Talks (an International seminar series promoting a diverse research exchange on autonomy), as well as a lead organizer for the seminal workshops “Compositional Robotics: Mathematics and Tools”, and “Co-Design and Coordination of Future Mobility Systems” at IEEE ICRA and ITSC, respectively. He is the recipient of a paper award at the 4th Applied Category Theory Conference, and of the Best Paper Award (1st Place) at the 24th IEEE International Conference on Intelligent Transportation Systems (ITSC).
ASL Publications
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D. Celestini, A. Afsharrad, D. Gammelli, T. Guffanti, G. Zardini, S. Lall, E. Capelli, S. D’Amico, and M. Pavone, “Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers,” in American Control Conference, 2025. (Submitted)
Abstract: Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging high-capacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30% cost improvement and 80% reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.
@inproceedings{CelestiniGammelliEtAl2025, author = {Celestini, D. and Afsharrad, A. and Gammelli, D. and Guffanti, T. and Zardini, G. and Lall, S. and Capelli, E. and {D'Amico}, S. and Pavone, M.}, title = {Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers}, booktitle = {{American Control Conference}}, year = {2025}, note = {Submitted}, keywords = {sub}, owner = {gammelli}, timestamp = {2024-10-29}, url = {https://arxiv.org/abs/2410.11723} } -
G. Zardini, N. Lanzetti, M. Pavone, and E. Frazzoli, “Analysis and Control of Autonomous Mobility-on-Demand Systems: A Review,” Annual Review of Control, Robotics, and Autonomous Systems, vol. 5, no. 1, pp. 633–658, 2022.
Abstract: Challenged by urbanization and increasing travel needs, existing transportation systems call for new mobility paradigms. In this article, we present the emerging concept of Autonomous Mobility-on-Demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems. Specifically, we first identify problem settings for their analysis and control, both from the operational and the planning perspective. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.
@article{ZardiniLanzettiEtAl2021, author = {Zardini, G. and Lanzetti, N. and Pavone, M. and Frazzoli, E.}, title = {Analysis and Control of Autonomous Mobility-on-Demand Systems: A Review}, journal = {{Annual Review of Control, Robotics, and Autonomous Systems}}, volume = {5}, number = {1}, pages = {633--658}, year = {2022}, url = {https://www.annualreviews.org/doi/abs/10.1146/annurev-control-042920-012811}, owner = {rdyro}, timestamp = {2022-02-05}, keywords = {pub} } -
G. Zardini, N. Lanzetti, A. Censi, E. Frazzoli, and M. Pavone, “Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions,” IEEE Transactions on Network Science and Engineering, 2022. (In Press)
Abstract: The design of future mobility solutions (autonomous vehicles, micromobility solutions, etc.) and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management policies. This requires tools to study such a coupling and co-design future mobility systems in terms of different objectives. This paper presents a framework to address such co-design problems. In particular, we leverage the recently developed mathematical theory of co-design to frame and solve the problem of designing and deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions such as shared bikes and e-scooters, and public transit, in terms of fleets sizing, vehicle characteristics, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity assumptions and it naturally handles multiple objectives, delivering the rational solutions on the Pareto front and thus enabling policy makers to select a policy. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess the costs and benefits of interventions, and that such analytical techniques might inform policy-making in the future.
@article{ZardiniEtAlBis2020, author = {Zardini, G. and Lanzetti, N. and Censi, A. and Frazzoli, E. and Pavone, M.}, title = {Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions}, journal = {{IEEE Transactions on Network Science and Engineering}}, year = {2022}, note = {In press}, url = {https://arxiv.org/pdf/2008.08975.pdf}, keywords = {press}, owner = {gzardini}, timestamp = {2020-08-21} } -
G. Zardini, N. Lanzetti, M. Salazar, A. Censi, E. Frazzoli, and M. Pavone, “On the Co-Design of AV-Enabled Mobility Systems,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Rhodes, Greece, 2020.
Abstract: The design of autonomous vehicles (AVs) and the design of AV-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AV-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.
@inproceedings{ZardiniEtAl2020, author = {Zardini, G. and Lanzetti, N. and Salazar, M. and Censi, A. and Frazzoli, E. and Pavone, M.}, title = {On the Co-Design of AV-Enabled Mobility Systems}, booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}}, year = {2020}, address = {Rhodes, Greece}, month = sep, url = {https://arxiv.org/abs/2003.04739}, owner = {gzardini}, timestamp = {2020-03-11} } -
G. Zardini, N. Lanzetti, M. Salazar, A. Censi, E. Frazzoli, and M. Pavone, “Towards a Co-Design Framework for Future Mobility Systems,” in Annual Meeting of the Transportation Research Board, Washington D.C., United States, 2020.
Abstract: The design of Autonomous Vehicles (AVs) and the design of AVs-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AVs-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity assumptions and it naturally handles multiple objectives, delivering the rational solutions on the Pareto front and thus enabling policy makers to select a solution through “political” criteria. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess the costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.
@inproceedings{ZardiniLanzettiEtAl2020, author = {Zardini, G. and Lanzetti, N. and Salazar, M. and Censi, A. and Frazzoli, E. and Pavone, M.}, title = {Towards a Co-Design Framework for Future Mobility Systems}, booktitle = {{Annual Meeting of the Transportation Research Board}}, year = {2020}, address = {Washington D.C., United States}, month = jan, url = {https://arxiv.org/pdf/1910.07714.pdf}, owner = {samauro}, timestamp = {2019-10-22} }