  Modern trajectory optimization based approaches to motion planning are fast,
easy to implement, and effective on a wide range of robotics tasks. However,
trajectory optimization algorithms have parameters that are typically set in
advance (and rarely discussed in detail). Setting these parameters properly can
have a significant impact on the practical performance of the algorithm,
sometimes making the difference between finding a feasible plan or failing at
the task entirely. We propose a method for leveraging past experience to learn
how to automatically adapt the parameters of Gaussian Process Motion Planning
(GPMP) algorithms. Specifically, we propose a differentiable extension to the
GPMP2 algorithm, so that it can be trained end-to-end from data. We perform
several experiments that validate our algorithm and illustrate the benefits of
our proposed learning-based approach to motion planning.
