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

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.