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

2026-09-24

AI Summary

This study addresses the challenges of state estimation, flight stability, and disturbance-robust precise manipulation in outdoor aerial operations by proposing an infrastructure-free autonomous aerial manipulation framework. Methodologically, the approach integrates Diffusion Policy for robust imitation learning, LiDAR-inertial odometry for onboard perception and state estimation, and whole-body model predictive control (MPC) to ensure flight stability and precise manipulation. Real-world experiments demonstrate that the system successfully eliminates reliance on motion capture systems, achieving end-to-end real-time manipulation tasks in authentic outdoor environments. This work overcomes the bottleneck of existing aerial manipulation approaches confined to controlled indoor settings, providing an effective solution for autonomous operations in complex, unstructured scenarios.

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.

Problem

• Aerial Manipulation
• Outdoor Environments
• State Estimation
• Whole-Body Control
• External Disturbances

Innovation

• Aerial Manipulation
• Diffusion Policy
• Whole-Body MPC
• LiDAR-Inertial Odometry
• Imitation Learning