Gioele Zardini

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Zardini

First Name

Gioele

ORCID

0000-0002-6601-2237

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Publications 1 - 10 of 26

A Compositional Sheaf-Theoretic Framework for Event-Based Systems
Zardini, Gioele; Spivak, David I.; Censi, Andrea; et al. (2021)
Electronic Proceedings in Theoretical Computer Science ~ Proceedings of the 3rd Annual International Applied Category Theory Conference 2020
A compositional sheaf-theoretic framework for the modeling of complex event-based systems is presented. We show that event-based systems are machines, with inputs and outputs, and that they can be composed with machines of different types, all within a unified, sheaf-theoretic formalism. We take robotic systems as an exemplar of complex systems and rigorously describe actuators, sensors, and algorithms using this framework.
Enhancing Efficiency and Reliability of Electric Vehicles via Adaptive E-Gear Control
Sandel, Luca; Zardini, Gioele; Mitrova, Sofija; et al. (2023)
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Battery Electric Vehicles (BEVs) offer a sus tainable alternative to Internal Combustion Engine Vehicles (ICEVs). This paper addresses some of the challenges faced by the automotive industry and the scientific community in defining the technology for the next generation of automotive power converters. The focus is on achieving an improved drivetrain’s energy efficiency, enhancing drivetrain reliability, while minimizing costs to enable large-scale adoption of BEVs and Hybrid Electric Vehicles (HEVs). The paper leverages an automotive converter equipped with the recently developed Adjustable Hybrid Switch (AHS) based electric gear and proposes a reliability-based control algorithm for operating the converter E-Gear (EG) of BEVs. By integrat ing reliability control principles, the proposed algorithm min imizes system damage over time and enhances the converter’s lifetime. The case studies, based on standardised driving cycles, demonstrate the benefits of the presented approach in terms of energy losses and lifetime expectations. Overall, this work contributes a novel approach to drivetrain control in BEVs, highlighting the potential of the proposed control strategy to improve energy efficiency and reliability. The research findings provide valuable insights for the development of next-generation automotive power converters.
Categorification of Negative Information using Enrichment
Censi, Andrea; Frazzoli, Emilio; Lorand, Jonathan; et al. (2023)
Electronic Proceedings in Theoretical Computer Science ~ Proceedings Fifth International Conference on Applied Category Theory (ACT 2022)
In many applications of category theory it is useful to reason about “negative information”. For example, in planning problems, providing an optimal solution is the same as giving a feasible solution (the “positive” information) together with a proof of the fact that there cannot be feasible solutions better than the one given (the “negative” information). We model negative information by introducing the concept of “norphisms”, as opposed to the positive information of morphisms. A “nategory” is a category that has “Nom”-sets in addition to hom-sets, and specifies the compatibility rules between norphisms and morphisms. With this setup we can choose to work in “coherent” “subnategories”: subcategories that describe a potential instantiation of the world in which all morphisms and norphisms are compatible. We derive the composition rules for norphisms in a coherent subnategory; we show that norphisms do not compose by themselves, but rather they need to use morphisms as catalysts. We have two distinct rules of the type morphism+norphism→norphism. We then show that those complex rules for norphism inference are actually as natural as the ones for morphisms, from the perspective of enriched category theory. Every small category is enriched over P = ⟨Set, ×, 1⟩. We show that we can derive the machinery of norphisms by considering an enrichment over a certain monoidal category called PN (for “positive”/“negative”). In summary, we show that an alternative to considering negative information using logic on top of the categorical formalization is to “categorify” the negative information, obtaining negative arrows that live at the same level as the positive arrows, and suggest that the new inference rules are born of the same substance from the perspective of enriched category theory.
Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
Zardini, Gioele; Censi, Andrea; Frazzoli, Emilio (2021)
2021 European Control Conference (ECC)
Designing cyber-physical systems is a complex task which requires insights at multiple abstraction levels. The choices of single components are deeply interconnected and need to be jointly studied. In this work, we consider the problem of co-designing the control algorithm as well as the platform around it. In particular, we leverage a monotone theory of codesign to formalize variations of the LQG control problem as monotone feasibility relations. We then show how this enables the embedding of control co-design problems in the higher level co-design problem of a robotic platform. We illustrate the properties of our formalization by analyzing the co-design of an autonomous drone performing search-and-rescue tasks and show how, given a set of desired robot behaviors, we can compute Pareto efficient design solutions.
Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics
Zanardi, Alessandro; Zardini, Gioele; Srinivasan, Sirish; et al. (2022)
IEEE Robotics and Automation Letters
Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hierarchical priorities of each player with the interactive nature of the environment. By contextualizing standard game theoretical notions, we provide two sufficient conditions on the preference of the players to prove existence of pure Nash Equilibria in finite action sets. Moreover, we define formal operations on the preference structures and link them to a refinement of the game solutions, showing how the set of equilibria can be systematically shrunk. The presented results are showcased in a driving game where autonomous vehicles select from a finite set of trajectories. The results demonstrate the interpretability of results in terms of minimum-rank-violation for each player.
Game Theory to Study Interactions between Mobility Stakeholders
Zardini, Gioele; Lanzetti, Nicolas; Guerrini, Laura; et al. (2021)
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Increasing urbanization and exacerbation of sustainability goals threaten the operational efficiency of current transportation systems and confront cities with complex choices with huge impact on future generations. At the same time, the rise of private, profit-maximizing Mobility Service Providers leveraging public resources, such as ride-hailing companies, entangles current regulation schemes. This calls for tools to study such complex socio-technical problems. In this paper, we provide a game-theoretic framework to study interactions between stakeholders of the mobility ecosystem, modeling regulatory aspects such as taxes and public transport prices, as well as operational matters for Mobility Service Providers such as pricing strategy, fleet sizing, and vehicle design. Our framework is modular and can readily accommodate different types of Mobility Service Providers, actions of municipalities, and low-level models of customers’ choices in the mobility system. Through both an analytical and a numerical case study for the city of Berlin, Germany, we showcase the ability of our framework to compute equilibria of the problem, to study fundamental tradeoffs, and to inform stakeholders and policy makers on the effects of interventions. Among others, we show tradeoffs between customers’ satisfaction, environmental impact, and public revenue, as well as the impact of strategic decisions on these metrics.
CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles
Milojevic, Dejan; Zardini, Gioele; Elser, Miriam; et al. (2025)
IEEE Transactions on Robotics
This paper discusses the integration challenges and strategies for designing mobile robots, by focusing on the task-driven, optimal selection of hardware and software to balance safety, efficiency, and minimal usage of resources such as costs, energy, computational requirements, and weight. We emphasize the interplay between perception and motion planning in decision-making by introducing the concept of occupancy queries to quantify the perception requirements for sampling-based motion planners. Sensor and algorithm performance are evaluated using False Negative Rate (FNR) and False Positive Rate (FPR) across various factors such as geometric relationships, object properties, sensor resolution, and environmental conditions. By integrating perception requirements with perception performance, an Integer Linear Programming (ILP) approach is proposed for efficient sensor and algorithm selection and placement. This forms the basis for a co-design optimization that includes the robot body, motion planner, perception pipeline, and computing unit. We refer to this framework for solving the co-design problem of mobile robots as CODEI, short for Co-design of Embodied Intelligence. A case study on developing an Autonomous Vehicle (AV) for urban scenarios provides actionable information for designers, and shows that complex tasks escalate resource demands, with task performance affecting choices of the autonomy stack. The study demonstrates that resource prioritization influences sensor choice: cameras are preferred for cost-effective and lightweight designs, while lidar sensors are chosen for better energy and computational efficiency.
On the Co-Design of AV-Enabled Mobility Systems
Zardini, Gioele; Lanzetti, Nicolas; Salazar, Mauro; et al. (2020)
2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
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.
Strategic Interactions in Multi-modal Mobility Systems: A Game-Theoretic Perspective
Zardini, Gioele; Lanzetti, Nicolas; Belgioioso, Giuseppe; et al. (2023)
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
The evolution of existing transportation systems, mainly driven by urbanization and increased availability of mobility options, such as private, profit-maximizing ride-hailing companies, calls for tools to reason about their design and regulation. To study this complex socio-technical problem, one needs to account for the strategic interactions of the heterogeneous stakeholders involved in the mobility ecosystem and analyze how they influence the system. In this paper, we focus on the interactions between citizens who compete for the limited resources of a mobility system to complete their desired trip. Specifically, we present a game-theoretic framework for multi-modal mobility systems, where citizens, characterized by heterogeneous preferences, have access to various mobility options and seek individually-optimal decisions. We study the arising game and prove the existence of an equilibrium, which can be efficiently computed via a convex optimization problem. Through both an analytical and a numerical case study for the classic scenario of Sioux Falls, USA, we illustrate the capabilities of our model and perform sensitivity analyses. Importantly, we show how to embed our framework into a “larger” game among stakeholders of the mobility ecosystem (e.g., municipality, Mobility Service Providers (MSPs), and citizens), effectively giving rise to tools to inform strategic interventions and policy-making in the mobility ecosystem.
Hierarchical Co-Design for Multi-Race Strategy Optimization in Formula 1
Neumann, Marc-Philippe; Habermacher, Raphael; Fieni, Giona; et al. (2025)
This work presents a hierarchical optimization framework for season-level decision-making in Formula 1, combining monotone co-design with sequential optimization. By integrating component design, energy deployment, and long-term degradation within a unified structure, the approach captures both local (lap and race) trade-offs and global (seasonal) constraints. The co-design layer generates track-dependent Pareto-optimal mappings between performance and wear, which are then composed into a finite-horizon optimization problem governing component usage and replacement decisions. Applied to a hybrid-electric Formula 1 power unit, the framework determines optimal battery sizing, deployment, and replacement timing across an entire season. Results show that exploiting regulatory constraints strategically, accepting local penalties to enable global gains, can increase cumulative championship points. Furthermore, while race order does not alter the attainable total reward, it modifies the optimal control policy, illustrating the temporal coupling inherent to multi-stage decision problems. Beyond motorsport, the proposed formulation provides a general template for long-horizon resource management and co-design of complex engineering systems.

Publications 1 - 10 of 26

Gioele Zardini

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