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2025

RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour

Serpiva, Valerii, Lykov, Artem, Myshlyaev, Artyom et al.

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RaceVLA presents an innovative approach for autonomous racing drone navigation by leveraging Visual-Language-Action (VLA) to emulate human-like behavior.

This research explores the integration of advanced algorithms that enable drones to adapt their navigation strategies based on real-time environmental feedback, mimicking the decision-making processes of human pilots.
The model, fine-tuned on a collected racing drone dataset, demonstrates strong generalization despite the complexity of drone racing environments.
RaceVLA outperforms OpenVLA in motion (75.0 vs 60.0) and semantic generalization (45.5 vs 36.3), benefiting from the dynamic camera and simplified motion tasks.

RaceVLA: VLA-based Racing Drone Navigation with Human-like Behaviour · Around

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