Add dataset description, task categories, and paper/project links

README.md

@@ -1,3 +1,54 @@
----
-license: mit
----
+---
+license: mit
+task_categories:
+- robotics
+tags:
+- reinforcement-learning
+- imitation-learning
+- robot-manipulation
+---
+
+# Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning (ZPRL)
+
+[**Project Page**](https://manutdmoon.github.io/ZPRL/) | [**Paper**](https://huggingface.co/papers/2605.19919) | [**GitHub**](https://github.com/manutdmoon/ZPRL)
+
+This repository contains the datasets used in the paper "Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning".
+
+## Dataset Description
+
+These datasets are prepared for training and evaluating robot manipulation policies using the ZPRL framework. They are based on the [Robomimic Multi-Human (MH) dataset](https://robomimic.github.io/docs/v0.3/datasets/overview.html) and include the following tasks:
+- **Can**
+- **Square**
+- **Transport**
+
+Each dataset consists of 100 trajectories randomly sampled from the original MH dataset. The data has been processed to include:
+1. **Image Observations**: Rendered image observations at the resolution required for training.
+2. **Absolute Actions**: Delta actions from the original datasets have been converted into absolute actions to facilitate training with flow-matching policies.
+
+### File Structure
+The datasets are provided in `.hdf5` format. The directory structure is organized as follows:
+```bash
+robomimicv030
+├── can
+│   └── mh
+│       └── image_v141_subset_abs.hdf5
+├── square
+│   └── mh
+│       └── image_v141_subset_abs.hdf5
+└── transport
+    └── mh
+        └── image_v141_subset_abs.hdf5
+```
+
+## Citation
+If you find this dataset or the associated code useful, please consider citing the following paper:
+```bibtex
+@misc{yu2026zprl,
+      title={Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning},
+      author={Dongjie Yu and Kun Lei and Zhennan Jiang and Jia Pan and Huazhe Xu},
+      year={2026},
+      eprint={2605.19919},
+      archivePrefix={arXiv},
+      url={https://arxiv.org/abs/2605.19919},
+}
+```