Add dataset card for RLA-WM (#1)

- Add dataset card for RLA-WM (ea21c3cd40a58941a99718bbfa4cc90ba63f326d)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -0,0 +1,46 @@
+---
+task_categories:
+- robotics
+---
+
+# Learning Visual Feature-Based World Models via Residual Latent Action
+
+This repository contains the dataset artifacts for the paper [Learning Visual Feature-Based World Models via Residual Latent Action](https://huggingface.co/papers/2605.07079).
+
+[Project page](https://mlzxy.github.io/rla-wm) | [GitHub](https://github.com/mlzxy/rla-wm)
+
+## Dataset Description
+
+The primary dataset included here is **Maniskill3DWorld**, which consists of multi-modal ManiSkill trajectories. It is designed for 3D and multi-view research and includes:
+- 7-camera RGB, depth, and masks
+- Animated robot meshes
+- Voxel point clouds
+
+## Sample Usage
+
+You can download and extract the ManiSkill dataset using the Hugging Face CLI:
+
+```bash
+# Create data directory
+mkdir -p data && cd data
+
+# Download the dataset
+hf download xyzhang368/RLA-WM --repo-type dataset --include "maniskill.tar" --local-dir . 
+
+# Extract the data
+tar -xf maniskill.tar
+```
+
+## Citation
+
+```bibtex
+@article{zhang2026learning,
+  title={{Learning Visual Feature-Based World Models via Residual Latent Action}},
+  author={Zhang, Xinyu and Xu, Zhengtong and Tao, Yutian and Wang, Yeping and She, Yu and Boularias, Abdeslam},
+  journal={arXiv preprint arXiv:2605.07079},
+  year={2026},
+  eprint={2605.07079},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV}
+}
+```