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.

生物系统中劳动分工现象无处不在，动物社会与多细胞生物中观察到的各类复杂任务特化均可佐证这一点。尽管劳动分工显然具备适应性，但其最初的演化路径仍不明朗——这是因为要实现所需的协调程度，需要同时兼具多项复杂性状以达成协同。近年来，进化群体机器人学（evolutionary swarm robotics）已成为研究群体层面协调行为演化的优质实验平台。本研究首次借助该框架，探究同质机器人群体中行为任务特化的演化起源。我们所研究的场景涉及劳动分工的高级形式——该形式常见于昆虫社会，被称为"任务分划（task partitioning）"，即两类任务需由不同个体按顺序完成。研究结果表明，当环境具备可被利用的特征、且能降低任务切换成本并提升群体净效率时，任务分划便更易被选择青睐；同时，若执行不同子任务的行为库可作为预适应的构建模块，那么最优的任务特化群体配比便更易达成。此外，我们还首次证实，仅需借助名为语法进化（Grammatical Evolution）的自然启发式演化方法，从最基础的低阶行为基元出发，即可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，无需预先提供关于全局物体获取任务应如何最优拆分为子任务的先验信息，即可实现劳动分工。我们讨论了该方法在工程化自适应机器人群体中的应用潜力，并结合自然界演化出复杂社会性与任务特化的潜在路径，对本研究结果进行了解读。