Add robotics task category and research links to dataset card

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

This PR improves the dataset card for the OGBench data used in the "Latent Particle World Models" paper. Specifically, I have:
- Added the `robotics` task category to the metadata.
- Included links to the official paper, project page, and GitHub repository.
- Added a BibTeX citation section for researchers.

This helps make the dataset more discoverable and provides context for how it was used in the study of object-centric world models.

README.md

@@ -1,5 +1,7 @@
 ---
 license: cc-by-4.0
+task_categories:
+- robotics
 tags:
 - robot
 - ogbench
@@ -9,6 +11,28 @@ tags:
 - simulation
 - manipulation
 ---
-Pre-processed 64x64 frames for the `scene` and `cube` tasks from the OGBench benchmark. Includes actions+frames.
 
-https://github.com/seohongpark/ogbench
+# OGBench Data for Latent Particle World Models (LPWM)
+
+This repository contains pre-processed 64x64 frames for the `scene` and `cube` tasks from the [OGBench benchmark](https://github.com/seohongpark/ogbench). The dataset includes actions and frames used for training and evaluating **Latent Particle World Models (LPWM)**.
+
+LPWM is a self-supervised object-centric world model that autonomously discovers keypoints, bounding boxes, and object masks directly from video data. It is designed to scale to real-world multi-object datasets and is applicable in decision-making tasks such as goal-conditioned imitation learning.
+
+- **Paper:** [Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling](https://huggingface.co/papers/2603.04553)
+- **Project Page:** [https://taldatech.github.io/lpwm-web](https://taldatech.github.io/lpwm-web)
+- **GitHub Repository:** [https://github.com/taldatech/lpwm](https://github.com/taldatech/lpwm)
+
+## Citation
+
+If you use this data or the LPWM model in your research, please cite the following paper:
+
+```bibtex
+@inproceedings{
+  daniel2026latent,
+  title={Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling},
+  author={Tal Daniel and Carl Qi and Dan Haramati and Amir Zadeh and Chuan Li and Aviv Tamar and Deepak Pathak and David Held},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=lTaPtGiUUc}
+}
+```