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

TL;DR

VLN-Pilot introduces a large vision-language model to autonomously navigate indoor drones by interpreting natural language instructions and visual data, enabling flexible, high-level control without extensive task-specific programming.

Contribution

This work presents the first framework integrating large vision-language models for autonomous indoor drone navigation based on natural language commands.

Findings

High success rates on complex instruction-following tasks

Effective long-horizon navigation with multiple semantic targets

Reduced operator workload and improved safety

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.