Uncrewed Aerial Vehicles (UAVs) have attracted significant attention in applications such as inspection and maintenance. Many of these tasks require aerial robots to physically interact with the environment, motivating the emerging field of aerial manipulation. However, most existing approaches focus on a single task with specialized hardware and control strategies, limiting their ability to generalize across tasks and environments.

This dissertation investigates how to achieve versatile aerial manipulation: enabling a general aerial manipulation platform to perform diverse manipulation tasks across different environments using a unified hardware and algorithmic pipeline.

We first study a fundamental capability for aerial manipulation: stable physical interaction with the environment. We integrate tactile sensing on a fully-actuated hexarotor and develop a flight controller that leverages contact feedback for stabilization. Building on this capability, we address a more general aerial interaction problem that requires simultaneous tracking of motion and contact force trajectories through a pipeline combining a contact-aware trajectory planner and a hybrid motion-force controller.

We then move beyond individual interaction tasks and develop a unified aerial manipulation framework for versatile task execution. The framework adopts an end-effector-centric interface that connects high-level policy and low-level control, enabling intuitive aerial teleoperation and learning from demonstrations. To further scale policy learning from human demonstrations, we propose Embodiment-Aware Diffusion Policy (EADP) for embodiment-aware deployment of embodiment-agnostic manipulation policies. By integrating a diffusion policy with an embodiment-specific controller during inference, this method guides trajectory generation toward dynamically feasible behaviors that respect the physical constraints of the aerial platform.

Finally, we introduce AM-Bench, an open-source simulation suite and benchmark for aerial manipulation. It provides a modular simulation environment supporting multiple aerial manipulator embodiments, diverse manipulation tasks, configurable disturbances, and standard control and imitation learning implementations, enabling reproducible research and systematic comparison of aerial manipulation algorithms.

Together, the general-purpose aerial manipulator platform, robust and precise control, and embodiment-aware policy learning presented in this dissertation progressively expand aerial robots from performing isolated interaction tasks to executing versatile manipulation behaviors across diverse scenarios. These advances move aerial robotics toward the long-term vision of versatile aerial manipulation, transforming aerial robots from passive flying sensors into active flying hands in the sky, capable of interacting with the world with intelligence and precision.