  With the continual adoption of Uncrewed Aerial Vehicles (UAVs) across a
wide-variety of application spaces, robust aerial manipulation remains a key
research challenge. Aerial manipulation tasks require interacting with objects
in the environment, often without knowing their dynamical properties like mass
and friction a priori. Additionally, interacting with these objects can have a
significant impact on the control and stability of the vehicle. We investigated
an approach for robust control and non-prehensile aerial manipulation in
unknown environments. In particular, we use model-based Deep Reinforcement
Learning (DRL) to learn a world model of the environment while simultaneously
learning a policy for interaction with the environment. We evaluated our
approach on a series of push tasks by moving an object between goal locations
and demonstrated repeatable behaviors across a range of friction values.
