Robot swarms often exhibit emergent behaviors that are fascinating to observe; however, it is often difficult to predict what swarm behaviors can emerge under a given set of agent capabilities. We seek to efficiently leverage human input to automatically discover a taxonomy of collective behaviors that can emerge from a particular multi-agent system, without requiring the human to know beforehand what behaviors are interesting or even possible. Our proposed approach adapts to user preferences by learning a similarity space over swarm collective behaviors using self-supervised learning and human-in-the-loop queries. We combine our learned similarity metric with novelty search and clustering to explore and categorize the space of possible swarm behaviors. We also propose several general-purpose heuristics that improve the efficiency of our novelty search by prioritizing robot controllers that are likely to lead to interesting emergent behaviors. We test our approach in simulation on two robot capability models and show that our methods consistently discover a richer set of emergent behaviors than prior work. Code, videos, and datasets are available at https://sites.google.com/view/evolving-novel-swarms.

翻译：机器人集群常展现出令人着迷的涌现行为；然而，在给定的智能体能力集合下，很难预测哪些集群行为可能涌现。我们旨在高效利用人类输入，自动发现特定多智能体系统中可能产生的集体行为分类，无需人类事先了解哪些行为有趣或甚至可能发生。我们提出的方法通过自监督学习和人类在环查询，学习集群集体行为的相似性空间，从而适应用户偏好。我们将学习到的相似性度量与新颖性搜索和聚类相结合，以探索和分类可能集群行为的空间。我们还提出了几种通用启发式方法，通过优先选择可能产生有趣涌现行为的机器人控制器，提高新颖性搜索的效率。我们在两个机器人能力模型的仿真中测试了该方法，并表明我们的方法始终能发现比先前工作更丰富的涌现行为集合。代码、视频和数据集可在 https://sites.google.com/view/evolving-novel-swarms 获取。