  In this work, we focus on the challenge of transferring an autonomous driving
controller from simulation to the real world (i.e. Sim2Real). We propose a
data-efficient method for online and on-the-fly adaptation of parametrizable
control architectures such that the target closed-loop performance is optimized
while accounting for uncertainties as model mismatches, changes in the
environment, and task variations. The novelty of the approach resides in
leveraging black-box optimization enabled by Executable Digital Twins (xDTs)
for data-driven parameter calibration through derivative-free methods to
directly adapt the controller in real-time. The xDTs are augmented with Domain
Randomization for robustness and allow for safe parameter exploration. The
proposed method requires a minimal amount of interaction with the real-world as
it pushes the exploration towards the xDTs. We validate our approach through
real-world experiments, demonstrating its effectiveness in transferring and
fine-tuning a NMPC with 9 parameters, in under 10 minutes. This eliminates the
need for hours-long manual tuning and lengthy machine learning training and
data collection phases. Our results show that the online adapted NMPC directly
compensates for the Sim2Real gap and avoids overtuning in simulation.
Importantly, a 75% improvement in tracking performance is achieved and the
Sim2Real gap over the target performance is reduced from a factor of 876 to
1.033.
