The discrepancy between simulation and reality-known as the reality gap-is one of the main challenges associated with using simulations to design control software for robot swarms. Currently, the reality-gap problem necessitates expensive and time consuming tests on physical robots to reliably assess control software. Predicting real-world performance accurately without recurring to physical experiments would be particularly valuable. In this paper, we compare various simulation-based predictors of the performance of robot swarms that have been proposed in the literature but never evaluated empirically. We consider (1) the classical approach adopted to estimate real-world performance, which relies on the evaluation of control software on the simulation model used in the design process, and (2) some so-called pseudo-reality predictors, which rely on simulation models other than the one used in the design process. To evaluate these predictors, we reuse 1021 instances of control software and their real-world performance gathered from seven previous studies. Results show that the pseudo-reality predictors considered yield more accurate estimates of the real-world performance than the classical approach.
© 2022. The Author(s).