  Efficient motion planning algorithms are of central importance for deploying
robots in the real world. Unfortunately, these algorithms often drastically
reduce the dimensionality of the problem for the sake of feasibility, thereby
foregoing optimal solutions. This limitation is most readily observed in agile
robots, where the solution space can have multiple additional dimensions.
Optimal control approaches partially solve this problem by finding optimal
solutions without sacrificing the complexity of the environment, but do not
meet the efficiency demands of real-world applications. This work proposes an
approach to resolve these issues simultaneously by training a machine learning
model on the outputs of an optimal control approach.
