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Quadrotor stabilizing controllers often require careful, model-specifictuning for safe operation. We use reinforcement learning to train policies insimulation that transfer remarkably well to multiple different physicalquadrotors. Our policies are low-level, i.e., we map the rotorcrafts' statedirectly to the motor outputs. The trained control policies are very robust toexternal disturbances and can withstand harsh initial conditions such asthrows. We show how different training methodologies (change of the costfunction, modeling of noise, use of domain randomization) might affect flightperformance. To the best of our knowledge, this is the first work thatdemonstrates that a simple neural network can learn a robust stabilizinglow-level quadrotor controller (without the use of a stabilizing PD controller)that is shown to generalize to multiple quadrotors.

Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors