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

Flying through a narrow gap using neural network: an end-to-end planning and control approach