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 a ttempt t o a pply r einforcement l earning t o 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.