Enabling vertical-stack proximal cooperation between multirotor flying robots can facilitate the execution of complex aerial manipulation tasks. However, vertical-stack proximal flight is commonly regarded as a dangerous condition that should be avoided because of persistent and intense downwash interference generated between flying robots^1,2. Here we propose a cooperative aerial manipulation system, called FlyingToolbox, that can work stably with sub-centimetre-level docking accuracy under vertical-stack flight conditions. The system consists of a toolbox micro-aerial vehicle (MAV) and a manipulator MAV. The robotic arm of the manipulator MAV can autonomously dock with a tool carried by the toolbox MAV, in which the docking accuracy reaches 0.80 ± 0.33 cm in the presence of downwash airflow of up to 13.18 m s⁻¹. By enabling midair tool exchange in proximity, FlyingToolbox resolves the paradox between flight proximity and manipulation accuracy, suggesting a new model for heterogeneous and interactive flying robot cooperation in diverse applications^{3, 4–5}.

An inherent fragility of quadrotor systems stems from model inaccuracies and external disturbances. These factors hinder performance and compromise the stability of the system, making precise control challenging. Existing model-based approaches either make deterministic assumptions, utilize Gaussian-based representations of uncertainty, or rely on nominal models, all of which often fall short in capturing the complex, multimodal nature of real-world dynamics. This work introduces DroneDiffusion, a novel framework that leverages conditional diffusion models to learn quadrotor dynamics, formulated as a sequence generation task. DroneDiffusion achieves superior generalization to unseen, complex scenarios by capturing the temporal nature of uncertainties and mitigating error propagation. We integrate the learned dynamics with an adaptive controller for trajectory tracking with stability guarantees. Extensive experiments in both simulation and real-world flights demonstrate the robustness of the framework across a range of scenarios, including unfamiliar flight paths and varying payloads, velocities, and wind disturbances. Project page: https://sites.google.com/view/dronediffusion.

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. In addition, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position subdynamics, the vehicle attitude subdynamics, and the manipulator subdynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches.