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Inter-individual differences are studied in natural systems, such as fish,bees, and humans, as they contribute to the complexity of both individual andcollective behaviors. However, individuality in artificial systems, such asrobotic swarms, is undervalued or even overlooked. Agent-specific deviationsfrom the norm in swarm robotics are usually understood as mere noise that canbe minimized, for example, by calibration. We observe that robots haveconsistent deviations and argue that awareness and knowledge of these can beexploited to serve a task. We measure heterogeneity in robot swarms caused byindividual differences in how robots act, sense, and oscillate. Our use case isKilobots and we provide example behaviors where the performance of robotsvaries depending on individual differences. We show a non-intuitive example ofphototaxis with Kilobots where the non-calibrated Kilobots show betterperformance than the calibrated supposedly ``ideal" one. We measure theinter-individual variations for heterogeneity in sensing and oscillation, too.We briefly discuss how these variations can enhance the complexity ofcollective behaviors. We suggest that by recognizing and exploring this newperspective on individuality, and hence diversity, in robotic swarms, we cangain a deeper understanding of these systems and potentially unlock newpossibilities for their design and implementation of applications.

Individuality in Swarm Robots with the Case Study of Kilobots: Noise, Bug, or Feature?