  In this paper, we investigate the problem of enabling a drone to fly through
a tilted narrow gap, without a traditional planning and control pipeline. To
this end, we propose an end-to-end policy network, which imitates from the
traditional pipeline and is fine-tuned using reinforcement learning. Unlike
previous works which plan dynamical feasible trajectories using motion
primitives and track the generated trajectory by a geometric controller, our
proposed method is an end-to-end approach which takes the flight scenario as
input and directly outputs thrust-attitude control commands for the quadrotor.
Key contributions of our paper are: 1) presenting an imitate-reinforce training
framework. 2) flying through a narrow gap using an end-to-end policy network,
showing that learning based method can also address the highly dynamic control
problem as the traditional pipeline does (see attached video:
https://www.youtube.com/watch?v=jU1qRcLdjx0). 3) propose a robust imitation of
an optimal trajectory generator using multilayer perceptrons. 4) show how
reinforcement learning can improve the performance of imitation learning, and
the potential to achieve higher performance over the model-based method.

Robotics