When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. However, the task of learning an individual policy to produce a desired emergent behavior remains a challenging and largely unsolved problem. We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in animals, we use an evolutionary algorithm to train a 'population' of individual behaviors to approximate a desired group behavior. We perform experiments using simulations of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics platforms conducted in the CoppeliaSim simulator. Additionally, we test on simulations of Anki Vector robots to display our algorithm's effectiveness on various modes of actuation. We evaluate our algorithm on various tasks where a somewhat complex group behavior is required for success. These tasks include an Area Coverage task, a Surround Target task, and a Wall Climb task. We compare behaviors evolved using our algorithm against 'designed policies', which we create in order to exhibit the emergent behaviors we desire.

翻译：在研究机器人群体时，许多研究观察到复杂的群体行为是由个体代理的简单局部行为涌现出来的。然而，学习单个策略以产生期望的涌现行为仍然是一个具有挑战性且基本未解决的问题。我们提出了一种训练分布式机器人群体算法以产生涌现行为的方法。受动物涌现行为生物进化的启发，我们使用进化算法训练一个“种群”的个体行为，以逼近期望的群体行为。我们在CoppeliaSim模拟器中对佐治亚理工学院的微型自主飞艇（GT-MABs）空中机器人平台进行仿真实验。此外，我们还在Anki Vector机器人的仿真上测试，以展示我们的算法在不同驱动模式下的有效性。我们在需要相当复杂群体行为才能成功的各项任务上评估我们的算法，这些任务包括区域覆盖任务、包围目标任务和爬墙任务。我们将使用我们的算法进化出的行为与“设计策略”进行比较，这些策略是我们为了展示期望的涌现行为而创建的。