  The paper presents a complete pipeline for learning continuous motion control
policies for a mobile robot when only a non-differentiable physics simulator of
robot-terrain interactions is available. The multi-modal state estimation of
the robot is also complex and difficult to simulate, so we simultaneously learn
a generative model which refines simulator outputs. We propose a coarse-to-fine
learning paradigm, where the coarse motion planning is alternated with
imitation learning and policy transfer to the real robot. The policy is jointly
optimized with the generative model. We evaluate the method on a real-world
platform in a batch of experiments.
