title: CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time
    Cognitive Task Solving and Reasoning in UAVs
summary: 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
paper_authors._id: 67c92e537ae0115c7a7b9fa3
paper_authors.name: Artem Lykov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T14:06:50.855Z
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paper_authors.avatarUrl: /avatars/d64661cf7e4159932540c5f9e8f30b31.svg
paper_authors.fullname: Artem Lykov
paper_authors.type: user
paper_authors.user: ArtemLykov
paper_authors._id: 67c92e537ae0115c7a7b9fa4
paper_authors.name: Valerii Serpiva
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-31T08:16:06.659Z
paper_authors._id: 67af5e10977325630584b1aa
paper_authors.avatarUrl: /avatars/c722f64ec7816f61dacefa2f1d550cdf.svg
paper_authors.fullname: Valerii Serpiva
paper_authors.type: user
paper_authors.user: SerValera
paper_authors._id: 67c92e537ae0115c7a7b9fa5
paper_authors.name: Muhammad Haris Khan
paper_authors._id: 67c92e537ae0115c7a7b9fa6
paper_authors.name: Oleg Sautenkov
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T14:07:35.797Z
paper_authors._id: 67bde90dd254a3945474bfef
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/uRvk8mrmhy8mNlgEpRpw9.png
paper_authors.fullname: Oleg Sautenkov
paper_authors.type: user
paper_authors.user: Sauten
paper_authors._id: 67c92e537ae0115c7a7b9fa7
paper_authors.name: Artyom Myshlyaev
paper_authors._id: 67c92e537ae0115c7a7b9fa8
paper_authors.name: Grik Tadevosyan
paper_authors._id: 67c92e537ae0115c7a7b9fa9
paper_authors.name: Yasheerah Yaqoot
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T14:07:22.304Z
paper_authors._id: 67a9c753b225716590053ea5
paper_authors.avatarUrl: /avatars/75facb21fe2fb3a8055e15d75cda0ed8.svg
paper_authors.fullname: Yasheerah Yaqoot
paper_authors.type: user
paper_authors.user: yasheerahyaqoot
paper_authors._id: 67c92e537ae0115c7a7b9faa
paper_authors.name: Dzmitry Tsetserukou
paper_title: CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time
    Cognitive Task Solving and Reasoning in UAVs
paper_summary: 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
paper_ai_summary: CognitiveDrone is a VLA model for UAVs that improves real-time action command generation in complex scenarios through the use of a reasoning module, achieving enhanced performance on cognitive tasks with the CognitiveDrone-R1 version.
paper_ai_keywords: vision-language-action model unmanned aerial vehicles real-time action commands vision-language model reasoning module CognitiveDroneBench benchmark cognitive tasks UAV control systems