FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation

IEEE Robotics and Automation Letters (RA-L), 2026  ·  Accepted January 21, 2026
Biyu Ye1,*   Na Fan2,*   Zhengping Fan1   Weiliang Deng1   Hongming Chen1   Qifeng Chen2   Ximin Lyu1,3,✉
* Co-first authors.  ·  ✉ Corresponding author.
1School of Intelligent Systems Engineering, Sun Yat-sen University, China  ·  2HKUST Visual Intelligence Lab, Hong Kong SAR  ·  3Differential Robotics Technology Company, Ltd., China
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UAV dodging a human-thrown tennis ball attack
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Abstract

Aerial manipulators (AMs) are gaining increasing attention in automated transportation and emergency services due to their superior dexterity compared to conventional multirotor drones. However, their practical deployment is challenged by the complexity of time-varying inertial parameters, which are highly sensitive to payload variations and manipulator configurations. Inspired by human strategies for interacting with unknown objects, this letter presents a novel onboard framework for robust aerial manipulation. The proposed system integrates a vision-based pre-grasp inertia estimation module with a post-grasp adaptation mechanism, enabling real-time estimation and adaptation of inertial dynamics. For control, we develop an inertia-aware adaptive control strategy based on gain scheduling, and assess its robustness via frequency-domain system identification. Our study provides new insights into post-grasp control for AMs, and real-world experiments validate the effectiveness and feasibility of the proposed framework.

Experiments

Exp 1
Inertial Parameter Estimation
Exp 2
Hover Stabilization
Exp 3
Object Grasping
Exp 3
Object Pick-and-Place
Exp 3
Object Transportation Under Disturbances
Last updated: 2026-01-21 · RA-L preprint project page for “FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation”
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