Data from: Evolution of self-organized task specialization in robot swarms

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior. Here we use this framework for the first time to study the evolutionary origin of behavioral task specialization among groups of identical robots. The scenario we study involves an advanced form of division of labor, common in insect societies and known as “task partitioning”, whereby two sets of tasks have to be carried out in sequence by different individuals. Our results show that task partitioning is favored whenever the environment has features that, when exploited, reduce switching costs and increase the net efficiency of the group, and that an optimal mix of task specialists is achieved most readily when the behavioral repertoires aimed at carrying out the different subtasks are available as pre-adapted building blocks. Nevertheless, we also show for the first time that self-organized task specialization could be evolved entirely from scratch, starting only from basic, low-level behavioral primitives, using a nature-inspired evolutionary method known as Grammatical Evolution. Remarkably, division of labor was achieved merely by selecting on overall group performance, and without providing any prior information on how the global object retrieval task was best divided into smaller subtasks. We discuss the potential of our method for engineering adaptively behaving robot swarms and interpret our results in relation to the likely path that nature took to evolve complex sociality and task specialization.

生物系统中的劳动分工无处不在，动物社会与多细胞生物中观测到的各类复杂任务特化现象，便是这一现象的有力佐证。尽管劳动分工显然具备适应性优势，但其最初的演化起源仍悬而未决——因为要达成所需的协调程度，需要多种复杂性状同时协同出现。近年来，进化群机器人学（evolutionary swarm robotics）已成为研究协调型群体行为演化的绝佳实验平台。本文首次借助该框架，探究由相同机器人组成的群体中行为任务特化的演化起源。我们所研究的场景，对应昆虫社会中常见的高级劳动分工形式——“任务划分（task partitioning）”：两类任务需由不同个体按顺序依次完成。研究结果显示，当环境具备可被利用的特征、能够降低任务切换成本并提升群体净效率时，任务划分便会成为更受青睐的策略；而当执行不同子任务的行为库可作为预适应的构建模块时，最易实现最优的任务特化者配比。此外，我们还首次证实：仅从基础的低级行为基元（behavioral primitives）出发，借助名为语法进化（Grammatical Evolution）的自然启发式演化方法，便可完全从零演化出自组织的任务特化机制。值得注意的是，仅通过选择整体群体性能即可实现劳动分工，无需预先提供任何关于如何将全局目标检索任务最优划分为若干子任务的先验信息。本文最后讨论了该方法在工程自适应机器人集群方面的应用潜力，并结合自然界演化出复杂社会性与任务特化的可能路径，对研究结果进行了解释。