  Accurate simulation of complex physical systems enables the development,
testing, and certification of control strategies before they are deployed into
the real systems. As simulators become more advanced, the analytical
tractability of the differential equations and associated numerical solvers
incorporated in the simulations diminishes, making them difficult to analyse. A
potential solution is the use of probabilistic inference to assess the
uncertainty of the simulation parameters given real observations of the system.
Unfortunately the likelihood function required for inference is generally
expensive to compute or totally intractable. In this paper we propose to
leverage the power of modern simulators and recent techniques in Bayesian
statistics for likelihood-free inference to design a control framework that is
efficient and robust with respect to the uncertainty over simulation
parameters. The posterior distribution over simulation parameters is propagated
through a potentially non-analytical model of the system with the unscented
transform, and a variant of the information theoretical model predictive
control. This approach provides a more efficient way to evaluate trajectory
roll outs than Monte Carlo sampling, reducing the online computation burden.
Experiments show that the controller proposed attained superior performance and
robustness on classical control and robotics tasks when compared to models not
accounting for the uncertainty over model parameters.
