# AeroGrab: A Unified Framework for Aerial Grasping in Cluttered Environments

arXiv: https://arxiv.org/abs/2603.15097 · HTML: https://mlsanity.com/paper/2603.15097/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2026.

## Built on by (most cited first, 2 of 2)

- [Reinforcement Learning with Inner-loop Dynamics Estimator for Aerial Manipulation under Uncertainty](https://mlsanity.com/paper/2606.16621/) (2026)
- [Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback](https://mlsanity.com/paper/2610.12549/) (2026)

## Abstract (arXiv)

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.