# Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis

arXiv: https://arxiv.org/abs/2011.10758 · HTML: https://mlsanity.com/paper/2011.10758/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2020.

## Built on by (most cited first, 7 of 7)

- [A Mathematical Characterization of Minimally Sufficient Robot Brains](https://mlsanity.com/paper/2308.09041/) (2023)
- [A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control Systems](https://mlsanity.com/paper/2204.00703/) (2022)
- [Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning](https://mlsanity.com/paper/2408.13376/) (2024)
- [To Compute or not to Compute? Adaptive Smart Sensing in Resource-Constrained Edge Computing](https://mlsanity.com/paper/2209.02166/) (2022)
- [CODEI: Resource-Efficient Task-Driven Co-Design of Perception and Decision Making for Mobile Robots Applied to Autonomous Vehicles](https://mlsanity.com/paper/2503.10296/) (2025)
- [Uncertainty-Aware End-to-End Co-Design of Neural Network Processors: From Training and Mapping to Fabrication](https://mlsanity.com/paper/2606.04850/) (2026)
- [Co-Design of Aeroelastic Systems with Deep Reinforcement Learning](https://mlsanity.com/paper/2607.15329/) (2026)

## Abstract (arXiv)

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.