Add model card and metadata for CosFly-Track

Hi, I'm Niels from the Hugging Face community science team. This PR improves the model card for the CosFly-Track project.

It adds:
- The `robotics` pipeline tag to the metadata.
- The `transformers` library name to enable code snippets.
- A link to the associated research paper: [CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization](https://huggingface.co/papers/2605.17776).
- A description of the dataset and the fine-tuned models used for UAV visual tracking.

README.md

@@ -1,7 +1,43 @@
 ---
 license: apache-2.0
+library_name: transformers
+pipeline_tag: robotics
 ---
 
+# CosFly-Track
 
+This repository contains model checkpoints associated with the paper [CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization](https://huggingface.co/papers/2605.17776).
 
+## Overview
 
+CosFly-Track is a large-scale multi-modal dataset and scalable generation pipeline designed for UAV (Unmanned Aerial Vehicle) visual tracking in urban environments. While most aerial vision-language navigation (VLN) datasets focus on static goals, CosFly-Track addresses the problem of continuously following a moving target while maintaining visibility and avoiding collisions.
+
+### Dataset Details
+- **Scale**: Approximately 12,000 expert and perturbed UAV trajectories.
+- **Volume**: 2.4 million timesteps (approximately 334 hours).
+- **Aligned Channels**: Seven aligned data channels including RGB, metric depth, semantic segmentation, six-degree-of-freedom (6-DoF) drone pose, target state with visibility flag, bilingual (Chinese-English) instructions, and trajectory-pair metadata.
+
+## Methodology
+
+The project introduces **MuCO** (Multi-Constraint Optimizer), a planner that plans directly in continuous 3D space. It jointly enforces target visibility, viewpoint quality, collision avoidance, smoothness, and kinematic feasibility, avoiding the artifacts of grid-based planners.
+
+## Model Description
+
+The checkpoints in this repository include various vision-language models (VLMs) fine-tuned on the CosFly-Track dataset to act as dynamic target-following agents. Evaluated architectures include:
+- Qwen (Qwen3-VL, Qwen3.5)
+- InternVL
+- GLM-4V
+- Gemma 4
+
+Fine-tuning on CosFly-Track significantly improves tracking performance (SR@1 meter) compared to zero-shot baselines, supporting the use of this dataset for training robust autonomous agents.
+
+## Citation
+
+```bibtex
+@article{cosflytrack2026,
+  title={CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization},
+  author={Anonymous Authors},
+  journal={arXiv preprint arXiv:2605.17776},
+  year={2026}
+}
+```