  Stochastic model predictive control has been a successful and robust control
framework for many robotics tasks where the system dynamics model is slightly
inaccurate or in the presence of environment disturbances. Despite the
successes, it is still unclear how to best adjust control parameters to the
current task in the presence of model parameter uncertainty and heteroscedastic
noise. In this paper, we propose an adaptive MPC variant that automatically
estimates control and model parameters by leveraging ideas from Bayesian
optimisation (BO) and the classical expected improvement acquisition function.
We leverage recent results showing that BO can be reformulated via density
ratio estimation, which can be efficiently approximated by simply learning a
classifier. This is then integrated into a model predictive path integral
control framework yielding robust controllers for a variety of challenging
robotics tasks. We demonstrate the approach on classical control problems under
model uncertainty and robotics manipulation tasks.
