  We present a nonlinear non-convex model predictive control approach to
solving a real-world labyrinth game. We introduce adaptive nonlinear
constraints, representing the non-convex obstacles within the labyrinth. Our
method splits the computation-heavy optimization problem into two layers;
first, a high-level model predictive controller which incorporates the full
problem formulation and finds pseudo-global optimal trajectories at a low
frequency. Secondly, a low-level model predictive controller that receives a
reduced, computationally optimized version of the optimization problem to
follow the given high-level path in real-time. Further, a map of the labyrinth
surface irregularities is learned. Our controller is able to handle the major
disturbances and model inaccuracies encountered on the labyrinth and
outperforms other classical control methods.
