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.

翻译：任务专业化可简化机器人行为并提升多机器人系统效率。先前研究已证明任务专业化在演化优化过程中的涌现性，但主要关注可行性而非成本。本研究以觅食场景为例，首次对机器人集群中的任务专业化进行成本效益分析。我们在有限评估预算内，为整体任务演化通用型人工神经网络行为，并为子任务演化任务专业化行为。研究表明，通用型行为可成功优化，而演化的任务专业化控制器未能实现高效协作，其性能反逊于通用型方案。因此，在优化预算受限时，任务专业化未必能提升系统效率。