CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time
  Cognitive Task Solving and Reasoning in UAVs
Authors: name: Artem Lykov, name: Valerii Serpiva, name: Muhammad Haris Khan, name: Oleg Sautenkov, name: Artyom Myshlyaev, name: Grik Tadevosyan, name: Yasheerah Yaqoot, name: Dzmitry Tsetserukou

This paper introduces CognitiveDrone, a novel Vision-Language-Action (VLA)
model tailored for complex Unmanned Aerial Vehicles (UAVs) tasks that demand
advanced cognitive abilities. Trained on a dataset comprising over 8,000
simulated flight trajectories across three key categories-Human Recognition,
Symbol Understanding, and Reasoning-the model generates real-time 4D action
commands based on first-person visual inputs and textual instructions. To
further enhance performance in intricate scenarios, we propose
CognitiveDrone-R1, which integrates an additional Vision-Language Model (VLM)
reasoning module to simplify task directives prior to high-frequency control.
Experimental evaluations using our open-source benchmark, CognitiveDroneBench,
reveal that while a racing-oriented model (RaceVLA) achieves an overall success
rate of 31.3%, the base CognitiveDrone model reaches 59.6%, and
CognitiveDrone-R1 attains a success rate of 77.2%. These results demonstrate
improvements of up to 30% in critical cognitive tasks, underscoring the
effectiveness of incorporating advanced reasoning capabilities into UAV control
systems. Our contributions include the development of a state-of-the-art VLA
model for UAV control and the introduction of the first dedicated benchmark for
assessing cognitive tasks in drone operations. The complete repository is
available at cognitivedrone.github.io