On the Cost of Evolving Task Specialization in Multi-Robot Systems

TL;DR

This paper investigates the trade-offs of task specialization in multi-robot systems, revealing that under limited optimization budgets, generalist behaviors outperform specialized ones in efficiency.

Contribution

It provides the first cost-benefit analysis of task specialization in robot swarms, highlighting limitations of specialization under constrained optimization resources.

Findings

Generalist behaviors can be effectively optimized.

Task-specialist controllers often fail to cooperate efficiently.

Specialization does not always lead to better performance with limited budgets.

Abstract

Task specialization can lead to simpler robot behaviors and higher efficiency in multi-robot systems. Previous works have shown the emergence of task specialization during evolutionary optimization, focusing on feasibility rather than costs. In this study, we take first steps toward a cost-benefit analysis of task specialization in robot swarms using a foraging scenario. We evolve artificial neural networks as generalist behaviors for the entire task and as task-specialist behaviors for subtasks within a limited evaluation budget. We show that generalist behaviors can be successfully optimized while the evolved task-specialist controllers fail to cooperate efficiently, resulting in worse performance than the generalists. Consequently, task specialization does not necessarily improve efficiency when optimization budget is limited.