Update dataset card with robotics metadata, paper links, and usage instructions

Hi, I'm Niels from the community science team at Hugging Face.

This PR improves the dataset card for ESARBench by:
- Adding the `robotics` task category and relevant tags to the metadata.
- Linking the research paper, project page, and GitHub repository.
- Providing a descriptive summary of the benchmark's features.
- Including installation and usage instructions to help researchers get started with the evaluation platform.

README.md

@@ -1,6 +1,81 @@
 ---
-license: mit
 language:
 - en
-pretty_name: ESAR
----
+license: mit
+pretty_name: ESARBench
+task_categories:
+- robotics
+tags:
+- uav
+- embodied-ai
+- search-and-rescue
+- mllm
+---
+
+# ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue
+
+[**Project Page**](https://4amgodvzx.github.io/ESAR.github.io) | [**Paper**](https://huggingface.co/papers/2605.01371) | [**GitHub**](https://github.com/4amGodvzx/ESAR)
+
+**ESARBench** is a comprehensive benchmark designed to evaluate Multimodal Large Language Model (MLLM) driven UAV agents in highly realistic **Embodied Search and Rescue (ESAR)** scenarios. It requires aerial agents to autonomously explore complex environments, identify rescue clues, and reason about victim locations to execute informed decision-making.
+
+## Key Features
+- **High Fidelity**: 4 large-scale environments built with UE5 + AirSim using real-world GIS data.
+- **Realistic Dynamics**: Integrated simulations of weather, lighting, and diverse rescue clues.
+- **Task Diversity**: 600 tasks modeled after real-world rescue cases.
+- **Evaluation**: A robust set of metrics to evaluate spatial memory, aerial adaptation, and search efficiency.
+
+## Quick Start
+
+### Installation
+
+1. Create a Python 3.9 environment using Conda:
+   ```bash
+   conda create -n esar_env python=3.9
+   conda activate esar_env
+   ```
+2. Install the required Python packages:
+   ```bash
+   pip install numpy msgpack-rpc-python pandas scipy dashscope opencv-python scikit-image scikit-fmm ultralytics matplotlib transformers pillow
+   ```
+3. Clone and install the AirSim-Colosseum client:
+   ```bash
+   git clone https://github.com/CodexLabsLLC/Colosseum.git
+   cd Colosseum/PythonClient
+   pip install -e . --no-build-isolation
+   ```
+
+### Running the Benchmark
+
+The platform scripts load sample tasks by default. To run the benchmark, navigate to the `source` directory and run the platform script corresponding to your OS:
+
+- **Linux**
+```bash
+cd source
+python main_platform_linux.py
+```
+- **Windows**
+```bash
+cd source
+python main_platform_win.py
+```
+
+### Analyzing Results
+Each task writes a log file to `log/task_<task_id>_log.json`. You can analyze the results using:
+```bash
+cd source
+python result_analysis.py
+```
+
+## Citation
+If you use ESARBench in your research, please cite:
+```bibtex
+@article{esarbench2024,
+  title={ESARBench: A Benchmark for Agentic UAV Embodied Search and Rescue},
+  author={Authors list here},
+  journal={arXiv preprint arXiv:2605.01371},
+  year={2024}
+}
+```
+
+## License
+This project is licensed under the MIT License.