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.

翻译：过驱动倾转旋翼平台相比传统固定臂无人机具有诸多优势，能够实现施加力与机器人姿态的解耦。这扩展了其应用领域至空中交互与操作，并允许其通过利用控制器可用的额外自由度来克服来自地面或墙壁效应等干扰。然而，过驱动也使控制问题复杂化，尤其是当倾斜臂的电机具有比旋转螺旋桨的电机更慢的动态特性时。我们并未构建一个考虑所有这些细微差别的复杂模型基控制器，而是尝试使用强化学习学习一个端到端的姿态控制器，并展示了其在惯性干扰和力干扰存在下相比最先进传统控制器的优越性能。