  Recently, Model Predictive Contouring Control (MPCC) has arisen as the
state-of-the-art approach for model-based agile flight. MPCC benefits from
great flexibility in trading-off between progress maximization and path
following at runtime without relying on globally optimized trajectories.
However, finding the optimal set of tuning parameters for MPCC is challenging
because (i) the full quadrotor dynamics are non-linear, (ii) the cost function
is highly non-convex, and (iii) of the high dimensionality of the
hyperparameter space. This paper leverages a probabilistic Policy Search method
- Weighted Maximum Likelihood (WML)- to automatically learn the optimal
objective for MPCC. WML is sample-efficient due to its closed-form solution for
updating the learning parameters. Additionally, the data efficiency provided by
the use of a model-based approach allows us to directly train in a
high-fidelity simulator, which in turn makes our approach able to transfer
zero-shot to the real world. We validate our approach in the real world, where
we show that our method outperforms both the previous manually tuned controller
and the state-of-the-art auto-tuning baseline reaching speeds of 75 km/h.
