  Purpose: Real-life applications using quadrotors introduce a number of
disturbances and time-varying properties that pose a challenge to flight
controllers. We observed that, when a quadrotor is tasked with picking up and
dropping a payload, traditional PID and RL-based controllers found in
literature struggle to maintain flight after the vehicle changes its dynamics
due to interaction with this external object.
  Methods: In this work, we introduce domain randomization during the training
phase of a low-level waypoint guidance controller based on Soft Actor-Critic.
The resulting controller is evaluated on the proposed payload pick up and drop
task with added disturbances that emulate real-life operation of the vehicle.
  Results & Conclusion: We show that, by introducing a certain degree of
uncertainty in quadrotor dynamics during training, we can obtain a controller
that is capable to perform the proposed task using a larger variation of
quadrotor parameters. Additionally, the RL-based controller outperforms a
traditional positional PID controller with optimized gains in this task, while
remaining agnostic to different simulation parameters.
