  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.
