We consider the problem of formally co-designing embodied intelligence as a whole, from hardware components such as chassis and sensors
to software modules such as control and perception pipelines. We propose a principled approach to formulate and solve complex embodied
intelligence co- design problems, leveraging a monotone co-design theory. The methods we propose are intuitive and integrate
heterogeneous engineering disciplines, allowing analytical and simulation- based modeling techniques and enabling interdisciplinarity.
We illustrate through a case study how, given a set of desired behaviors, our framework is able to compute Pareto efficient solutions
for the entire hardware and software stack of a self- driving vehicle.
@article{zardini2020formal,
title={A Formal Approach to the Co-Design of Embodied Intelligence},
author={Zardini, Gioele and Milojevic, Dejan and Censi, Andrea and Frazzoli, Emilio},
journal={arXiv preprint arXiv:2011.10756},
year={2020}
}
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 char- acteristics, 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{zardini2020camod,
title={Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions},
author={Zardini, Gioele and Lanzetti, Nicolas and Censi, Andrea and Frazzoli, Emilio and Pavone, Marco},
journal={arXiv preprint arXiv:2008.08975},
year={2020}
}
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 co- design 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.
@article{zardini2020co,
title={Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis},
author={Zardini, Gioele and Censi, Andrea and Frazzoli, Emilio},
journal={arXiv preprint arXiv:2011.10758},
year={2020}
}
G. Zardini, N. Lanzetti, M. Salazar, A. Censi, E. Frazzoli, M. Pavone On the Co-Design of AV-Enabled Mobility Systems
IEEE 23rd International Conference on Intelligent Transportation Systems Conference (ITSC), 2020, Rhodes (Greece)
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={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
year = {2020},
address = {Rhodes, Greece},
month = sep
doi={10.1109/ITSC45102.2020.9294499}
}
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.
@inproceedings{zardini2020compositional,
title={A Compositional Sheaf-Theoretic Framework for Event-Based Systems},
author={Zardini, Gioele and Spivak, David I and Censi, Andrea and Frazzoli, Emilio},
booktitle={3rd Annual International Applied Category Theory Conference (ACT 2020)},
year={2020}
}
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{zardini2020towards,
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 = {99th Annual Meeting of the Transportation Research Board},
year = {2020},
address = {Washington D.C., United States},
month = jan
}
@inproceedings{lanzetti2019self,
title={Do Self-driving Cars Swallow Public Transport? A Game-theoretical Perspective on Transportation Systems},
author={Lanzetti, Nicolas and Zardini, Gioele and Schiffer, Maximilian and Ostrovsky, Michael and Pavone, Marco},
booktitle={INFORMS Annual Meeting 2019},
year={2019}
}
Dissertations
G. Zardini A Co-Design Framework for Future Mobility Systems
Master's Thesis, Autonomous Systems Lab, Stanford University, 2019
Advisors: Dr. Andrea Censi, Prof. Emilio Frazzoli, Prof. Marco Pavone
G. Zardini Towards Task-Driven Closed-Loop Auto-Tuning of Dynamic Vision Sensors
Semester's Thesis, Institute for Dynamic Systems and Control, ETH Zurich, 2018
Advisors: Dr. Jacopo Tani, Prof. Emilio Frazzoli,
G. Zardini An Inductance-Based Sensor for Haptic Feedback Control
Bachelor's Thesis, Multi-Scale Robotics Lab, ETH Zurich, 2017
Advisors: Dr. Georgios Chatzipirpiridis, Prof. Bradley Nelson
Patents
T. Bonanni, G. Zardini, and F. Seccamonte (equal contribution) System and Method for Updating Map Data
USA, 2020.