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Language-guided Drone Control
Explore the use of large vision language models to control a drone.
Keywords: Human-drone interaction, large language models (LLMs), Robotics
Imagine controlling a drone with simple, natural language instructions like "fly through the gap" or "follow that red car” – this is the vision behind language-guided drone control. However, translating natural language instructions into precise drone maneuvers presents a unique challenge. Drones operate in a dynamic environment, requiring real-time interpretation of user intent and the ability to adapt to unforeseen obstacles.
Imagine controlling a drone with simple, natural language instructions like "fly through the gap" or "follow that red car” – this is the vision behind language-guided drone control. However, translating natural language instructions into precise drone maneuvers presents a unique challenge. Drones operate in a dynamic environment, requiring real-time interpretation of user intent and the ability to adapt to unforeseen obstacles.
This project focuses on developing a novel system for language-guided drone control using recent advances in Vision Language Models (VLMs). Our goal is to bridge the gap between human language and drone actions. We aim to create a system that can understand natural language instructions, translate them into safe and efficient flight instructions, and control the drone accordingly, making it accessible to a wider range of users and enabling more intuitive human-drone interaction.
This project focuses on developing a novel system for language-guided drone control using recent advances in Vision Language Models (VLMs). Our goal is to bridge the gap between human language and drone actions. We aim to create a system that can understand natural language instructions, translate them into safe and efficient flight instructions, and control the drone accordingly, making it accessible to a wider range of users and enabling more intuitive human-drone interaction.