On the Cost of Evolving Task Specialization in Multi-Robot Systems
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.
Cite
@article{arxiv.2603.09552,
title = {On the Cost of Evolving Task Specialization in Multi-Robot Systems},
author = {Paolo Leopardi and Heiko Hamann and Jonas Kuckling and Tanja Katharina Kaiser},
journal= {arXiv preprint arXiv:2603.09552},
year = {2026}
}
Comments
Accepted for publication in the proceeding of ANTS 2026 - 15th International Conference on Swarm Intelligence