Simto(Multi)Real: Transfer of LowLevel Robust Control Policies to Multiple Quadrotors Quadrotor stabilizing controllers often require careful, modelspecific tuning for safe operation. We use reinforcement learning to train policies in simulation that transfer remarkably well to multiple different physical quadrotors. Our policies are lowlevel, i.e., we map the rotorcraftsxe2x80x99 state directly to the motor outputs. The trained control policies are very robust to external disturbances and can withstand harsh initial conditions such as throws. We show how different training methodologies (change of the cost function, modeling of noise, use of domain randomization) might affect flight performance. To the best of our knowledge, this is the first work that demonstrates that a simple neural network can learn a robust stabilizing lowlevel quadrotor controller (without the use of a stabilizing PD controller) that is shown to generalize to multiple quadrotors. The video of our experiments can be found at https:sites.google.comviewsimtomultiquad.

Flying through a narrow gap using neural network: an endtoend planning and control approach 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 endtoend policy network, which imitates from the traditional pipeline and is finetuned 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 endtoend approach which takes the flight scenario as input and directly outputs thrustattitude control commands for the quadrotor. Key contributions of our paper are: 1) presenting an imitatereinforce training framework. 2) flying through a narrow gap using an endtoend policy network, showing that learning based method can also address the highly dynamic control problem as the traditional pipeline does (see attached video1). 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 modelbased method.