  This work presents a case study of a learning-based approach for target
driven map-less navigation. The underlying navigation model is an end-to-end
neural network which is trained using a combination of expert demonstrations,
imitation learning (IL) and reinforcement learning (RL). While RL and IL suffer
from a large sample complexity and the distribution mismatch problem,
respectively, we show that leveraging prior expert demonstrations for
pre-training can reduce the training time to reach at least the same level of
performance compared to plain RL by a factor of 5. We present a thorough
evaluation of different combinations of expert demonstrations, different RL
algorithms and reward functions, both in simulation and on a real robotic
platform. Our results show that the final model outperforms both standalone
approaches in the amount of successful navigation tasks. In addition, the RL
reward function can be significantly simplified when using pre-training, e.g.
by using a sparse reward only. The learned navigation policy is able to
generalize to unseen and real-world environments.
