CAPTURER: A Dexterous Aerial Manipulator for Dynamic and Precise Grasping across Diverse Environments

Meng Wang; Jindou Jia; Sicheng Zhou; Zeshuai Chen; Kexin Guo; Xiang Yu; Lei Guo

cs.ROOct 29, 2026

CAPTURER: A Dexterous Aerial Manipulator for Dynamic and Precise Grasping across Diverse Environments

Authors: Meng Wang, Jindou Jia, Sicheng Zhou, Zeshuai Chen, Kexin Guo, Xiang Yu, Lei Guo

Organizations: School of Automation Science and Electrical Engineering, Beihang University, Beijing, China · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang, Singapore · Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119077, Singapore ·

Abstract

As a representative form of embodied robotic interaction, aerial grasping requires close coordination between the aerial platform and manipulator. Most existing systems perform aerial grasping with static or slowly moving targets under fixed scenarios. Dynamic and precise grasping across different conditions introduces additional challenges, including target motion, localization drift, dynamic coupling, and external disturbances. In this work, we present CAPTURER, a dexterous aerial manipulator for centimeter-level grasping across diverse environments. To achieve the challenging goal, we first develop an onboard perception system for state estimation and target detection. Drift correction and global pose transformation are introduced to improve target localization during flight. A cooperative motion planner is proposed to coordinate the mobility of the aerial platform with the dexterity of the manipulator according to the relative target state. To maintain grasping accuracy under disturbances, we design a disturbance-rejection controller to compensate for dynamic coupling, model uncertainty, and external disturbances. The capability of CAPTURER is validated through indoor and outdoor experiments with both static and moving targets. The results demonstrate that CAPTURER achieves centimeter-level grasping accuracy for a marker pen measuring only 1.5 cm in diameter at flight speeds exceeding 1.5 m/s.

Figures & tables

Figure 1: Overview of CAPTURER workflow. Body : CAPTURER integrates onboard perception, cooperative motion planning, and anti-disturbance control within a unified framework. Perception : The perception module combines visual–inertial state estimation, target detection, drift correction, global pose transformation, motion prediction, and active target search to provide consistent robot and target states. Planning : Based on the relative target state, the cooperative planner generates coordinated motion references for the aerial platform and manipulator while accounting for the predicted platform attitude and their different motion characteristics. Control : The dynamic controller reconstructs and compensates the strong coupling effects induced by manipulator motion and employs residual observers to handle model uncertainty and external disturbances. The resulting framework supports indoor and outdoor grasping of stationary and moving targets using onboard sensing and online replanning. More details, including CAD models and source code, are available on the Project Site .

Ref.	Platform	Target		Perception		Plan	Control
		Motion	Type	Robot	Target	Replan capability *	Disturbance rejection †
A. Hovering grasping of stationary targets
Seo et al. (2017)	Hexarotor + serial arm	Static	Cylinder	Mocap	Visual	✓	✗
Bucki et al. (2023)	Quadrotor + folding arms	Static	Box	Mocap	Known	✗	✗
Wang et al. (2024b)	Quadrotor + serial arm	Static	Marker pen	Mocap	Mocap	✓	✗
Cao et al. (2025a)	Quadrotor + Delta arm	Static	Block	Mocap	Mocap	✗	✓

Table 1: Comparison of aerial manipulation systems.

Symbols	Physical interpretations
$m_{s}\in\mathbb{R}$	Total mass of the aerial manipulator
$\bm{J}\in\mathbb{R}^{3\times 3}$	Inertia matrix of the aerial platform
$p_{b}\in\mathbb{R}^{3}$	The aerial platform position in $\{\mathcal{I}\}$
$p_{e}\in\mathbb{R}^{3}$	The end-effector position in $\{\mathcal{I}\}$
$p_{ed}\in\mathbb{R}^{3}$	The end-effector position relative to the manipulator base, expressed in $\{\mathcal{D}\}$
$v_{b}\in\mathbb{R}^{3}$	The UAV linear velocity in $\{\mathcal{I}\}$

Table 2: Nomenclature.

Figure 3: Schematic of the serial manipulator.

Figure 4: Visualization of the operational capability.

Joint	$\vartheta_{i}$	$r_{i}$ (mm)	Mass (g)
1	$\begin{bmatrix}0&0&1\end{bmatrix}^{\top}$	$\begin{bmatrix}0&0&77\end{bmatrix}^{\top}$	210
2	$\begin{bmatrix}0&1&0\end{bmatrix}^{\top}$	$\begin{bmatrix}0&0&77\end{bmatrix}^{\top}$	146
3	$\begin{bmatrix}0&1&0\end{bmatrix}^{\top}$	$\begin{bmatrix}24&0&205\end{bmatrix}^{\top}$	138
4	$\begin{bmatrix}0&1&0\end{bmatrix}^{\top}$	$\begin{bmatrix}148&0&205\end{bmatrix}^{\top}$	336

Table 3: The manipulator system parameters.

Figure 5: (a) CoM shift along $Z$ -axis. (b) CoM shift along $Y$ -axis. (c) The aerial platform is oscillating when the manipulator moves.

Figure 6: The evaluation of CoM position during manipulation motion.

Figure 7: Performance comparison between state estimation via onboard sensors and ground truth in three trajectories. (a) Circle trajectory. (b) Lemniscate trajectory. (c) Four-leaf trajectory.

Figure 8: (a) CAPTURER detects a fixed target during flight. (b) CAPTURER detects a target that is moving randomly. (c) The position estimation results of the moving target. (d) The predicted velocity output of the moving target. (e) Detection error characteristics. The horizontal axis represents the moving velocity of the target and the vertical axis denotes the distance between the detected target and CAPTURER. (f) The process of searching for a target outside the camera’s FoV.

Figure 9: Estimation of $\ddot{P}_{bm}^{B}$ using different differentiation methods.

Figure 10: Estimation results of the coupling torque.

	Test I									Test II						Test III
	$T=12$			$T=8$			$T=6.28$			$T=12,\omega$ =3			$T=8,\omega$ =3			$T=12,\omega$ =3, wind disturbance
Methods	$\epsilon$	$\mu$	$\varrho$	$\epsilon$	$\mu$	$\varrho$	$\epsilon$	$\mu$	$\varrho$	$\epsilon$	$\mu$	$\varrho$	$\epsilon$	$\mu$	$\varrho$	$\epsilon$	$\mu$	$\varrho$
Baseline I	8.41	4.10	1.72	15.01	7.79	2.56	24.79	11.89	4.37	11.97	4.91	2.32	15.58	8.44	2.84	13.07	5.26	2.40
Baseline II	6.91	3.45	1.43	12.37	6.25	2.16	23.52	13.10	4.87	10.76	3.92	1.92	17.18	7.32	3.52	11.48	4.56	2.24
Baseline III	5.04	2.46	0.89	12.22	4.21	1.82	26.31	9.67	4.26	9.70	3.63	1.78	13.96	6.58	2.78	12.58	3.81	1.93
Our scheme	4.85	1.86	0.96	6.38	2.32	1.21	15.59	5.75	2.60	5.79	2.43	0.99	10.66	5.44	1.88	7.31	2.84	1.16

Table 4: Tracking error statistics of different control schemes (Unit: cm).

Figure 11: Baseline I can track the desired trajectory. However, the tracking performance substantially degrades with the increase of the coupling effects. The dynamical performance of Baseline II is enhanced due to the compensation of the first-order disturbance terms. Baseline III with the offline initialization training process achieves better results than Baseline I and Baseline II. By combining the established coupling model and residual observers, our scheme demonstrates the excellent dynamic performance among all experiments.

Figure 13: CAPTURER actively detects and grasps a target, then precisely places it into the specified area. The experimental video can be found in the Supplementary Movie S2.

Figure 14: (a) Process of detecting unknown objects. (b) Process of aerial grasp and place. (c) Position tracking error of the aerial platform. (d) The manipulator joints are regulated to grasp the target.

Figure 15: Sequential snapshots of the aerial grasping in an open lawn. The experimental video can be found in the Supplementary Movie S3.

Figure 16: (a) The distribution of state drift across different times. (b) Estimation results of residual disturbance torque. (c) Position tracking error of the aerial platform along three axes. (d) The manipulator state is continuously regulated to compensate for the UAV tracking error.

Figure 17: Key snapshots of the dynamic aerial grasping. The experimental video can be found in the Supplementary Movie S4.

Figure 18: Poses of CAPTURER in the $X$ - $Z$ plane, where the brown box indicates the captured object.

Figure 19: Key snapshots of the dynamic grasping of a moving object. The experimental video can be found in the Supplementary Movie S5.

Figure 20: Poses of CAPTURER in the $X$ - $Z$ plane, where the gray box indicates the target motion process.

Figure 21: The relative motion between CAPTURER and the target. The instant of starting to close the gripper is denoted by a gray circle. The red dashed line denotes the target velocity.

Figure 22: Evolution of the relative distance and motion velocity.

Figure 23: Representative scenarios that evaluate object adaptability, angular tolerance, and vertical reachability. The experimental video can be found in the Supplementary Movie S6.

Figure 24: The aerial platform is required to track a four-leaf trajectory with different lap times while the manipulator is fixed to the underside of the system. (a-c) Tracking error distribution of all methods in Group \raisebox{-0.20ex}{\scriptsize1}⃝, \raisebox{-0.20ex}{\scriptsize2}⃝, and \raisebox{-0.20ex}{\scriptsize3}⃝.

Figure 25: The aerial platform is required to track a four-leaf trajectory with different lap times while the manipulator follows a prescribed trajectory. (a) Key snapshots of the flight setup. (b) Tracking error distribution of all methods in Group \raisebox{-0.20ex}{\scriptsize4}⃝. (c) Tracking error distribution of all methods in Group \raisebox{-0.20ex}{\scriptsize5}⃝. (d)-(e) The evolution of $P_{bm}^{B}$ and $\dot{P}_{bm}^{B}$ . (f)-(g) The estimation of $\hat{F}_{m}$ and $\hat{\tau}_{m}$ . (h) Estimation result of disturbance torque in the rotational loop. (i) The dynamic tracking performance of the rotational loop.

Figure 26: (a) Experimental snapshot. (b) The wind field. (c) Tracking error distribution. (d) The position tracking performance. (e) The attitude tracking performance. (f) Estimation results of disturbance torque in the rotational loop.

Figure 27: The end-effector position tracking errors in Section 7.3 .

Figure 28: The end-effector position tracking errors in Section 7.4 .

Figure 29: The end-effector position tracking errors in Section 7.5 .

Figure 30: The end-effector position tracking errors in Section 7.6 .

Figure 31: Online optimization time at each replanning step during aerial grasping.

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Reliable aerial grasping in cluttered environments remains challenging due to occlusions and collision risks. Existing aerial manipulation pipelines largely rely on centroid-based grasping and lack integration between the grasp pose generation models, active exploration, and language-level task specification, resulting in the absence of a complete end-to-end system. In this work, we present an integrated pipeline for reliable aerial grasping in cluttered environments. Given a scene and a language instruction, the system identifies the target object and actively explores it to gain better views of the object. During exploration, a grasp generation network predicts multiple 6-DoF grasp candidates for each view. Each candidate is evaluated using a collision-aware feasibility framework, and the overall best grasp is selected and executed using standard trajectory generation and control methods. Experiments in cluttered real-world scenarios demonstrate robust and reliable grasp execution, highlighting the effectiveness of combining active perception with feasibility-aware grasp selection for aerial manipulation.

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