在有限优化预算下，任务专精反而降低机器人集群效率。
On the Cost of Evolving Task Specialization in Multi-Robot Systems
- 用神经网络演化通用与专精行为，对比性能差异
- 专精控制器协作差，整体表现不如通用策略
- 适合关注机器人协同优化成本的研究者
任务专精可使机器人行为更简单、系统更高效。以往研究仅验证其可行性，未评估代价。本文在觅食场景中，基于有限评估预算，演化通用行为与子任务专精行为。结果表明，通用行为可有效优化，而专精控制器协作效率低下，性能反而劣于通用策略。因此，在优化预算受限时，任务专精未必提升效率。
原文摘要 · Abstract (English)
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.
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