Aerial manipulation control is challenging due to the inherently coupled and highly variable dynamics involved in simultaneously controlling both the drone platform and its manipulator. Traditional model-based methods have been widely used but are limited by their dependence on accurate dynamics models, high computational cost, and sensitivity to external disturbances. Recent studies have explored reinforcement learning (RL)-based methods to overcome these limitations. However, most of them focus on fully actuated drones or rigid-link manipulators, thereby avoiding the complexity of underactuated platforms and manipulator control. This work develops a robust RL-based controller framework capable of whole-body control of an underactuated aerial manipulator with a two-degree-of-freedom arm.
The method is evaluated in both simulation and real-world experiments on three tasks: end-effector pose control, payload carrying, and path following. We achieve 6-DoF end-effector pose tracking with errors of 5.3 cm and 8.8°, with inference times of 0.18 ms, and demonstrate payload carrying capacity of up to 140 g through real-world experiments.