Aerial Manipulation in the Wild with Onboard Perception, Policy Learning, and Whole-Body Control

Abstract:Aerial manipulation in outdoor environments remains challenging due to the simultaneous requirements of reliable state estimation, stable aerial motion, and precise manipulation under external disturbances. In this work, we present a real-world outdoor aerial manipulation framework that integrates imitation learning, onboard LiDAR-inertial state estimation, and whole-body model predictive control. A Diffusion Policy is trained from manipulation demonstrations to generate desired end-effector motions from onboard observations. These learned commands are executed by a whole-body MPC that jointly coordinates the aerial platform and manipulator to realize the desired end-effector trajectory. To eliminate reliance on external motion-capture infrastructure, the platform employs onboard LiDAR-inertial odometry for state estimation during outdoor operation. We validate the complete framework on a physical aerial manipulator and demonstrate successful execution of outdoor manipulation tasks. The experimental results show that demonstration-driven manipulation policies can be effectively integrated with onboard state estimation and model-based whole-body control to enable aerial manipulation beyond controlled indoor environments.

5 pages, 4 figures. Accepted by 5th Workshop on Mobile Manipulation and Embodied Intelligence (MoMA.v5) at IROS 2026

Robotics (cs.RO)

arXiv:2609.30521 [cs.RO]

(or arXiv:2609.30521v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.30521 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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