DroneVLA: VLA based Aerial Manipulation

• URL: http://arxiv.org/abs/2601.13809v2
• Date: Wed, 21 Jan 2026 10:32:20 GMT
• Title: DroneVLA: VLA based Aerial Manipulation
• Authors: Fawad Mehboob, Monijesu James, Amir Habel, Jeffrin Sam, Miguel Altamirano Cabrera, Dzmitry Tsetserukou,
• Abstract summary: This work introduces a novel concept of autonomous aerial manipulation system capable of interpreting high-level natural language commands to retrieve objects and deliver them to a human user.<n>The system is intended to integrate a MediaPipe based on Grounding DINO and a Vision-Language-Action model with a custom-built drone equipped with a 1-DOF gripper and an Intel RealSense RGB-D camera.<n>We demonstrate the system's efficacy through real-world experiments for localization and navigation, which resulted in a 0.164m, 0.070m, and 0.084m of max, mean euclidean, and root-mean squared
• Score: 2.1645011609137295
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: As aerial platforms evolve from passive observers to active manipulators, the challenge shifts toward designing intuitive interfaces that allow non-expert users to command these systems naturally. This work introduces a novel concept of autonomous aerial manipulation system capable of interpreting high-level natural language commands to retrieve objects and deliver them to a human user. The system is intended to integrate a MediaPipe based on Grounding DINO and a Vision-Language-Action (VLA) model with a custom-built drone equipped with a 1-DOF gripper and an Intel RealSense RGB-D camera. VLA performs semantic reasoning to interpret the intent of a user prompt and generates a prioritized task queue for grasping of relevant objects in the scene. Grounding DINO and dynamic A* planning algorithm are used to navigate and safely relocate the object. To ensure safe and natural interaction during the handover phase, the system employs a human-centric controller driven by MediaPipe. This module provides real-time human pose estimation, allowing the drone to employ visual servoing to maintain a stable, distinct position directly in front of the user, facilitating a comfortable handover. We demonstrate the system's efficacy through real-world experiments for localization and navigation, which resulted in a 0.164m, 0.070m, and 0.084m of max, mean euclidean, and root-mean squared errors, respectively, highlighting the feasibility of VLA for aerial manipulation operations.

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