# Co-Design of Embodied Intelligence: A Structured Approach

arXiv: https://arxiv.org/abs/2011.10756 · HTML: https://mlsanity.com/paper/2011.10756/

## 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)

- [Categorical semantics of compositional reinforcement learning](https://mlsanity.com/paper/2208.13687/) (2022)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning](https://mlsanity.com/paper/2408.13376/) (2024)
- [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)
- [Embodied AI in Machine Learning -- is it Really Embodied?](https://mlsanity.com/paper/2505.10705/) (2025)
- [Knowledge Engineering for Wind Energy](https://mlsanity.com/paper/2310.00804/) (2023)
- [The body is not there to compute: Comment on "Informational embodiment: Computational role of information structure in codes and robots" by Pitti et al](https://mlsanity.com/paper/2512.22868/) (2025)

## Abstract (arXiv)

We consider the problem of co-designing embodied intelligence as a whole in a structured way, from hardware components such as propulsion systems 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.