  The well-known quote from George Box states that: "All models are wrong, but
some are useful." To develop more useful models, we quantify the inaccuracy
with which a given model represents a system of interest, so that we may
leverage this quantity to facilitate controller synthesis and verification.
Specifically, we develop a procedure that identifies a sim2real gap that holds
with a minimum probability. Augmenting the nominal model with our identified
sim2real gap produces an uncertain model which we prove is an accurate
representor of system behavior. We leverage this uncertain model to synthesize
and verify a controller in simulation using a probabilistic verification
approach. This pipeline produces controllers with an arbitrarily high
probability of realizing desired safe behavior on system hardware without
requiring hardware testing except for those required for sim2real gap
identification. We also showcase our procedure working on two hardware
platforms - the Robotarium and a quadruped.
