Exploring Self-Assembling Behaviors in a Swarm of Bio-micro-robots using Surrogate-Assisted MAP-Elites

TL;DR

This paper introduces a surrogate-assisted MAP-Elites algorithm to optimize self-assembling behaviors in micro-robot swarms, significantly reducing computational costs while achieving high-quality, diverse solutions.

Contribution

It presents a novel surrogate-assisted MAP-Elites approach for controlling molecular robot swarms, outperforming previous methods in efficiency and diversity.

Findings

MAP-Elites outperforms previous optimization approaches.

Surrogate model reduces simulation costs by half.

Method achieves similar accuracy with less computational effort.

Abstract

Swarms of molecular robots are a promising approach to create specific shapes at the microscopic scale through self-assembly. However, controlling their behavior is a challenging problem as it involves complex non-linear dynamics and high experimental variability. Hand-crafting a molecular controller will often be time-consuming and give sub-optimal results. Optimization methods, like the bioNEAT algorithm, were previously employed to partially overcome these difficulties, but they still had to cope with deceptive high-dimensional search spaces and computationally expensive simulations. Here, we describe a novel approach to solve this problem by using MAP-Elites, an algorithm that searches for both high-performing and diverse solutions. We then apply it to a molecular robotic framework we recently introduced that allows sensing, signaling and self-assembly at the micro-scale and show that MAP-Elites outperforms previous approaches. Additionally, we propose a surrogate model of micro-robots physics and chemical reaction dynamics to reduce the computational costs of simulation. We show that the resulting methodology is capable of optimizing controllers with similar accuracy as when using only a full-fledged realistic model, with half the computational budget.