Add paper link, project page, and task category

README.md

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 ---
+task_categories:
+- robotics
 dataset_info:
   features:
   - name: id
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   - split: validation
     path: data/validation-*
 ---
+
+# DeliveryBench
+
+[**Project Page**](https://deliverybench.github.io) | [**Paper**](https://huggingface.co/papers/2512.19234) | [**Code**](https://github.com/SCAI-JHU/SimWorld-Robotics)
+
+DeliveryBench is a city-scale embodied benchmark grounded in the real-world profession of food delivery. It is designed to evaluate the long-horizon planning and constraint-aware decision-making capabilities of LLM and VLM-based agents in realistic, procedurally generated 3D environments.
+
+### Dataset Summary
+
+Agents in DeliveryBench must navigate procedurally generated 3D cities to maximize net profit while managing diverse constraints, such as delivery deadlines, transportation expenses, vehicle battery, and interactions with other couriers and customers. The environment includes:
+- **Diverse Road Networks and Buildings**: Multiple cities with functional locations and various transportation modes.
+- **Realistic Resource Dynamics**: Systematic evaluation of constraint-aware planning in a realistic, resource-dense environment.
+
+### Dataset Structure
+
+The dataset contains trajectories of agents navigating these environments, including:
+- `current_view`: Visual observation from the agent's perspective.
+- `expected_view`: The target visual state for the subtask.
+- `plan`: Natural language reasoning or planning steps associated with the trajectory.
+- `action`: Discrete actions including moving forward, turning, or completing a subtask.
+- `metadata`: Information regarding orientation, distance, and subtask history.
+
+## Citation
+
+If you use this dataset in your research, please cite:
+```bibtex
+@article{deliverybench2025,
+  title={DeliveryBench: Can Agents Earn Profit in Real World?},
+  author={Authors list not provided},
+  journal={arXiv preprint arXiv:2512.19234},
+  year={2025}
+}
+```