研究发现：机器人越多，专精分工越省评估成本。
The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems
- 对比专精与通用型机器人的演化效率，分析评估预算分配
- 机器人数量增加时，专精方案所需总评估预算更低
- 适合对多机器人系统演化效率感兴趣的科研人员
任务专精可提升多机器人系统（MRS）的效率。以往研究通过进化优化探索了任务专精型机器人控制器的涌现，并认为当子任务行为作为构建模块易于获取时，专精更易演化。然而，可用评估预算需分配给所有子任务，而单一通用行为可独占全部预算进行优化。本文在基于物理的机器人仿真中，分析了在觅食场景下演化任务专精型与通用型行为的成本收益。研究发现，随着多机器人系统规模增大，所需总评估预算降低，专精型行为即可超越通用型表现。
原文摘要 · Abstract (English)
Task specialization can improve the efficiency of multi-robot systems (MRSs). Previous works have investigated the emergence of task-specialist robot controllers through evolutionary optimization and have argued that task specialization is more likely to evolve when subtask behaviors are readily available as building blocks. However, the available evaluation budget must be distributed across all subtasks, whereas a single generalist behavior can exploit the entire budget for its own optimization. We present a cost-benefit analysis of evolving task-specialist versus generalist behaviors in a foraging scenario here. In a physics-based robotics simulator, we study the total evaluation budget required to evolve task-specialist behaviors that outperform generalist behaviors across MRS sizes. We show that with increasing MRS size, a lower total evaluation budget is sufficient to evolve specialists that outperform generalists.
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