Add task category and paper link to dataset card

This PR adds the robotics task category to the dataset and ensures the dataset can be found at https://huggingface.co/papers/2503.01378.

README.md

@@ -1,31 +1,37 @@
-# CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time Cognitive Task Solving and Reasoning in UAVs
-
-![Teaser](teaser.png)
-
-## Abstract
-
-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](https://cognitivedrone.github.io/).
-
-## Dataset Structure
-
-- **data/rlds/** – Data for training and validation in RLDS format:
-  - `train/` – Training data.
-
-- **data/benchmark/** – Data for the simulation benchmark:
-  - `validation` – JSON files for model evaluation.
-
-## Instructions for Use
-
-1. **Training:** Use the data from `data/rlds/train/` for model training.
-2. **Evaluation:** Run the simulation benchmark using the files from `data/benchmark/validation/`.
-
-## Links and Bibliography
-
-- **Project Repository:** [CognitiveDrone](https://cognitivedrone.github.io/)
-- **Paper:** BibTeX reference will be available soon (coming soon).
-
----
-
-This provides you with a complete package of files that can be easily uploaded to Hugging Face. Once the repository is created on the platform, you can use the `git lfs` command for large files if necessary.
-
-If you have any further questions regarding repository organization or the dataset card, feel free to ask!
+---
+task_categories:
+- robotics
+---
+
+# CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time Cognitive Task Solving and Reasoning in UAVs
+
+[![Teaser](teaser.png)](https://huggingface.co/papers/2503.01378)
+
+## Abstract
+
+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](https://cognitivedrone.github.io/).
+
+## Dataset Structure
+
+- **data/rlds/** – Data for training and validation in RLDS format:
+  - `train/` – Training data.
+
+- **data/benchmark/** – Data for the simulation benchmark:
+  - `validation` – JSON files for model evaluation.
+
+## Instructions for Use
+
+1. **Training:** Use the data from `data/rlds/train/` for model training.
+2. **Evaluation:** Run the simulation benchmark using the files from `data/benchmark/validation/`.
+
+## Links and Bibliography
+
+- **Project Repository:** [CognitiveDrone](https://cognitivedrone.github.io/)
+- **Paper:** [CognitiveDrone: A VLA Model and Evaluation Benchmark for Real-Time Cognitive Task Solving and Reasoning in UAVs](https://huggingface.co/papers/2503.01378)
+- **Paper:** BibTeX reference will be available soon (coming soon).
+
+---
+
+This provides you with a complete package of files that can be easily uploaded to Hugging Face. Once the repository is created on the platform, you can use the `git lfs` command for large files if necessary.
+
+If you have any further questions regarding repository organization or the dataset card, feel free to ask!