  Recently, needs for unmanned aerial vehicles (UAVs) that are attachable to
the wall have been highlighted. As one of the ways to address the need,
researches on various tilting multirotors that can increase maneuverability has
been employed. Unfortunately, existing studies on the tilting multirotors
require considerable amounts of prior information on the complex dynamic model.
Meanwhile, reinforcement learning on quadrotors has been studied to mitigate
this issue. Yet, these are only been applied to standard quadrotors, whose
systems are less complex than those of tilting multirotors. In this paper, a
novel reinforcement learning-based method is proposed to control a tilting
multirotor on real-world applications, which is the first attempt to apply
reinforcement learning to a tilting multirotor. To do so, we propose a novel
reward function for a neural network model that takes power efficiency into
account. The model is initially trained over a simulated environment and then
fine-tuned using real-world data in order to overcome the sim-to-real gap
issue. Furthermore, a novel, efficient state representation with respect to the
goal frame that helps the network learn optimal policy better is proposed. As
verified on real-world experiments, our proposed method shows robust
controllability by overcoming the complex dynamics of tilting multirotors.
