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

TL;DR

This study empirically examines how computation budgets affect the co-optimization of robot design and control in simulation, revealing that training resource allocation impacts design complexity and overall performance.

Contribution

It provides experimental insights into the challenges and effects of computation budget allocation during co-optimization of robot design and control.

Findings

Reducing controller training improves subsequent performance.

Lower training budgets lead to simpler robot designs.

Controller retraining with more resources enhances performance.

Abstract

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.