We study the problem of determining the emergent behaviors that are possible given a functionally heterogeneous swarm of robots with limited capabilities. Prior work has considered behavior search for homogeneous swarms and proposed the use of novelty search over either a hand-specified or learned behavior space followed by clustering to return a taxonomy of emergent behaviors to the user. In this paper, we seek to better understand the role of novelty search and the efficacy of using clustering to discover novel emergent behaviors. Through a large set of experiments and ablations, we analyze the effect of representations, evolutionary search, and various clustering methods in the search for novel behaviors in a heterogeneous swarm. Our results indicate that prior methods fail to discover many interesting behaviors and that an iterative human-in-the-loop discovery process discovers more behaviors than random search, swarm chemistry, and automated behavior discovery. The combined discoveries of our experiments uncover 23 emergent behaviors, 18 of which are novel discoveries. To the best of our knowledge, these are the first known emergent behaviors for heterogeneous swarms of computation-free agents. Videos, code, and appendix are available at the project website: https://sites.google.com/view/heterogeneous-bd-methods

翻译：我们研究在具有功能异构但能力受限的机器人集群中，如何确定可能涌现出的行为。已有工作针对同构集群提出了行为搜索方法，通过在手工指定或学习得到的行为空间上进行新颖性搜索，再通过聚类向用户返回涌现行为的分类体系。本文旨在深入理解新颖性搜索的作用以及利用聚类发现新型涌现行为的有效性。通过大规模实验与消融分析，我们研究了异构集群中行为发现任务中表征方式、进化搜索及多种聚类方法的影响。结果表明，现有方法难以发现许多有意义的行为，而引入人类反馈的迭代发现过程比随机搜索、群体化学方法及自动化行为发现能识别出更多行为。我们的实验共发现23种涌现行为，其中18种为首次报道。据我们所知，这是针对无计算能力代理构成的异构集群所发现的首批涌现行为。相关视频、代码及附录详见项目网站：https://sites.google.com/view/heterogeneous-bd-methods