VLN-Pilot: Large Vision-Language Model as an Autonomous Indoor Drone Operator

• URL: http://arxiv.org/abs/2602.05552v1
• Date: Thu, 05 Feb 2026 11:23:11 GMT
• Title: VLN-Pilot: Large Vision-Language Model as an Autonomous Indoor Drone Operator
• Authors: Bessie Dominguez-Dager, Sergio Suescun-Ferrandiz, Felix Escalona, Francisco Gomez-Donoso, Miguel Cazorla,
• Abstract summary: VLN-Pilot is a framework in which a large Vision-and-Language Model assumes the role of a human pilot for indoor drone navigation.<n>Our framework integrates language-driven semantic understanding with visual perception, enabling context-aware, high-level flight behaviors.
• Score: 1.4878644292213625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces VLN-Pilot, a novel framework in which a large Vision-and-Language Model (VLLM) assumes the role of a human pilot for indoor drone navigation. By leveraging the multimodal reasoning abilities of VLLMs, VLN-Pilot interprets free-form natural language instructions and grounds them in visual observations to plan and execute drone trajectories in GPS-denied indoor environments. Unlike traditional rule-based or geometric path-planning approaches, our framework integrates language-driven semantic understanding with visual perception, enabling context-aware, high-level flight behaviors with minimal task-specific engineering. VLN-Pilot supports fully autonomous instruction-following for drones by reasoning about spatial relationships, obstacle avoidance, and dynamic reactivity to unforeseen events. We validate our framework on a custom photorealistic indoor simulation benchmark and demonstrate the ability of the VLLM-driven agent to achieve high success rates on complex instruction-following tasks, including long-horizon navigation with multiple semantic targets. Experimental results highlight the promise of replacing remote drone pilots with a language-guided autonomous agent, opening avenues for scalable, human-friendly control of indoor UAVs in tasks such as inspection, search-and-rescue, and facility monitoring. Our results suggest that VLLM-based pilots may dramatically reduce operator workload while improving safety and mission flexibility in constrained indoor environments.

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