Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, such as robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood as mere noise that can be minimized, for example, by calibration. We observe that robots have consistent deviations and argue that awareness and knowledge of these can be exploited to serve a task. We measure heterogeneity in robot swarms caused by individual differences in how robots act, sense, and oscillate. Our use case is Kilobots and we provide example behaviors where the performance of robots varies depending on individual differences. We show a non-intuitive example of phototaxis with Kilobots where the non-calibrated Kilobots show better performance than the calibrated supposedly ``ideal" one. We measure the inter-individual variations for heterogeneity in sensing and oscillation, too. We briefly discuss how these variations can enhance the complexity of collective behaviors. We suggest that by recognizing and exploring this new perspective on individuality, and hence diversity, in robotic swarms, we can gain a deeper understanding of these systems and potentially unlock new possibilities for their design and implementation of applications.

翻译：自然系统（如鱼类、蜜蜂和人类）中的个体间差异已被广泛研究，这些差异促成了个体与群体行为的复杂性。然而，在人工系统（如机器人集群）中，个体性常被低估甚至忽视。在群体机器人中，偏离标准的特定代理偏差通常被视为可最小化的噪声（例如通过校准加以消除）。我们观察到机器人存在一致性偏差，并主张这些偏差的认知与利用可用于任务执行。我们测量了因机器人行动、感知和振荡模式差异导致的集群异质性。以Kilobot为用例，我们展示了机器人性能因个体差异而变化的示例行为。通过非直觉的光趋性实验，我们发现未校准的Kilobot反而比校准后的"理想"个体表现更优。同时，我们也测量了感知与振荡中的个体间异质性，并简要讨论了这些差异如何增强群体行为的复杂性。我们提出：通过识别并探索机器人集群中个体性（即多样性）这一新视角，可以更深入理解此类系统，并可能为应用设计与实现开辟新的可能性。