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.
@article{arxiv.2601.22686,
title = {FlyAware: Inertia-Aware Aerial Manipulation via Vision-Based Estimation and Post-Grasp Adaptation},
author = {Biyu Ye and Na Fan and Zhengping Fan and Weiliang Deng and Hongming Chen and Qifeng Chen and Ximin Lyu},
journal= {arXiv preprint arXiv:2601.22686},
year = {2026}
}