  Overactuated tilt-rotor platforms offer many advantages over traditional
fixed-arm drones, allowing the decoupling of the applied force from the
attitude of the robot. This expands their application areas to aerial
interaction and manipulation, and allows them to overcome disturbances such as
from ground or wall effects by exploiting the additional degrees of freedom
available to their controllers. However, the overactuation also complicates the
control problem, especially if the motors that tilt the arms have slower
dynamics than those spinning the propellers. Instead of building a complex
model-based controller that takes all of these subtleties into account, we
attempt to learn an end-to-end pose controller using reinforcement learning,
and show its superior behavior in the presence of inertial and force
disturbances compared to a state-of-the-art traditional controller.
