Learning a Single Near-hover Position Controller for Vastly Different Quadcopters

Understand

This paper proposes an adaptive near-hover position controller for quadcopters, which can be deployed to quadcopters of very different mass, size and motor constants, and also shows rapid adaptation to unknown disturbances during runtime.

• The core algorithmic idea is to learn a single policy that can adapt online at test time not only to the disturbances applied to the drone, but also to the robot dynamics and hardware in the same framework.
• We achieve this by training a neural network to estimate a latent representation of the robot and environment parameters, which is used to condition the behaviour of the controller, also represented as a neural network.
• We train both networks exclusively in simulation with the goal of flying the quadcopters to goal positions and avoiding crashes to the ground.