  Learning from demonstration for motion planning is an ongoing research topic.
In this paper we present a model that is able to learn the complex mapping from
raw 2D-laser range findings and a target position to the required steering
commands for the robot. To our best knowledge, this work presents the first
approach that learns a target-oriented end-to-end navigation model for a
robotic platform. The supervised model training is based on expert
demonstrations generated in simulation with an existing motion planner. We
demonstrate that the learned navigation model is directly transferable to
previously unseen virtual and, more interestingly, real-world environments. It
can safely navigate the robot through obstacle-cluttered environments to reach
the provided targets. We present an extensive qualitative and quantitative
evaluation of the neural network-based motion planner, and compare it to a
grid-based global approach, both in simulation and in real-world experiments.
