Add robotics task category and research links to dataset card

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}
+}
+```