# An Empirical Study on the Computation Budget of Co-Optimization of Robot Design and Control in Simulation

arXiv: https://arxiv.org/abs/2409.08621 · HTML: https://mlsanity.com/paper/2409.08621/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Embodied Intelligence via Learning and Evolution](https://mlsanity.com/paper/2102.02202/) (2021)
- [Plan Online, Learn Offline: Efficient Learning and Exploration via Model-Based Control](https://mlsanity.com/paper/1811.01848/) (2018)
- [Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots](https://mlsanity.com/paper/2201.09863/) (2022)
- [Scalable Co-Optimization of Morphology and Control in Embodied Machines](https://mlsanity.com/paper/1706.06133/) (2017)
- [Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning](https://mlsanity.com/paper/1911.06832/) (2019)
- [Data-efficient Learning of Morphology and Controller for a Microrobot](https://mlsanity.com/paper/1905.01334/) (2019)
- [MAP-Elites enables Powerful Stepping Stones and Diversity for Modular Robotics](https://mlsanity.com/paper/2012.04375/) (2020)
- [The Effects of Learning in Morphologically Evolving Robot Systems](https://mlsanity.com/paper/2111.09851/) (2021)
- [Morpho-evolution with learning using a controller archive as an inheritance mechanism](https://mlsanity.com/paper/2104.04269/) (2021)
- [Comparing Two Samples Through Stochastic Dominance: A Graphical Approach](https://mlsanity.com/paper/2203.07889/) (2022)
- [On the Fair Comparison of Optimization Algorithms in Different Machines](https://mlsanity.com/paper/2305.07345/) (2023)
- [Speeding-up Evolutionary Algorithms to solve Black-Box Optimization Problems](https://mlsanity.com/paper/2309.13349/) (2023)
- [Evolving Robots on Easy Mode: Towards a Variable Complexity Controller for Quadrupeds](https://mlsanity.com/paper/1902.04403/) (2019)
- [Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better](https://mlsanity.com/paper/2309.13099/) (2023)

## Abstract (arXiv)

The design (shape) of a robot is usually decided before the control is implemented. This might limit how well the design is adapted to a task, as the suitability of the design is given by how well the robot performs in the task, which requires both a design and a controller. The co-optimization or simultaneous optimization of the design and control of robots addresses this limitation by producing a design and control that are both adapted to the task. This paper investigates some of the challenges inherent in the co-optimization of design and control in simulation. The results show that reducing how well the controllers are trained during the co-optimization process significantly improves the robot's performance when considering a second phase in which the controller for the best design is retrained with additional resources. In addition, the results demonstrate that the computation budget allocated to training the controller for each design influences design complexity, with simpler designs associated with lower training budgets. This paper experimentally studies key questions discussed in other works in the literature on the co-optimization of design and control of robots in simulation in four different co-optimization problems.