  With an increasing use of data-driven models to control robotic systems, it
has become important to develop a methodology for validating such models before
they can be deployed to design a controller for the actual system.
Specifically, it must be ensured that the controller designed for a learned
model would perform as expected on the actual physical system. We propose a
context-specific validation framework to quantify the quality of a learned
model based on a distance measure between the closed-loop actual system and the
learned model. We then propose an active sampling scheme to compute a
probabilistic upper bound on this distance in a sample-efficient manner. The
proposed framework validates the learned model against only those behaviors of
the system that are relevant for the purpose for which we intend to use this
model, and does not require any a priori knowledge of the system dynamics.
Several simulations illustrate the practicality of the proposed framework for
validating the models of real-world systems, and consequently, for controller
synthesis.
