Update dataset card with paper link, metadata, and usage instructions

README.md

@@ -1,16 +1,66 @@
----
-license: apache-2.0
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-
-# RoboMemArena
-
-## Task Categories
-
-This dataset contains four categories of tasks:
-
-| Category | Description | Task IDs |
-|---|---|---|
-| **Multi-Sequence** | Tasks requiring execution of multiple sequential sub-steps | Task1, Task2, Task3, Task22 |
-| **Multi-Occlusion** | Tasks involving occluded or hidden objects | Task4, Task5, Task11, Task12, Task13, Task14, Task17, Task20, Task21, Task23, Task24 |
-| **Multi-Counting** | Tasks requiring repeated actions, such as pouring twice | Task6, Task7, Task8, Task9, Task10, Task15, Task16 |
-| **Multi-Transferring** | Tasks involving transferring objects between locations | Task18, Task19, Task25, Task26 |
+---
+license: apache-2.0
+task_categories:
+- robotics
+---
+
+# RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark
+
+[**Project Page**](https://robomemarena.github.io/) | [**Paper**](https://huggingface.co/papers/2605.10921) | [**GitHub**](https://github.com/OpenHelix-Team/RoboMemArena)
+
+RoboMemArena is a large-scale benchmark of 26 tasks designed to evaluate robotic memory. It features average trajectory lengths exceeding 1,000 steps per task, with 68.9% of subtasks being memory-dependent. The benchmark covers various domains including multi-sequence execution, occlusion handling, counting, and object transferring.
+
+## Task Categories
+
+This dataset contains four categories of tasks:
+
+| Category | Description | Task IDs |
+|---|---|---|
+| **Multi-Sequence** | Tasks requiring execution of multiple sequential sub-steps | Task1, Task2, Task3, Task22 |
+| **Multi-Occlusion** | Tasks involving occluded or hidden objects | Task4, Task5, Task11, Task12, Task13, Task14, Task17, Task20, Task21, Task23, Task24 |
+| **Multi-Counting** | Tasks requiring repeated actions, such as pouring twice | Task6, Task7, Task8, Task9, Task10, Task15, Task16 |
+| **Multi-Transferring** | Tasks involving transferring objects between locations | Task18, Task19, Task25, Task26 |
+
+## Dataset Structure
+
+The dataset is organized into category folders, each containing task subfolders. The demonstration data is provided in HDF5 format with keyframe annotations.
+
+```
+<dataset_root>/
+├── <category_1>/
+│   └── 1_cookies_tomato_basket_dataset/
+│       └── subtask_data/         # Keyframe-annotated HDF5 episodes
+│           ├── pick_cookies_0_seed100_task1.hdf5
+│           ├── pick_cookies_0_seed101_task1.hdf5
+│           └── ...
+```
+
+Each HDF5 file in `subtask_data/` contains:
+- `data/demo_{id}/actions`: (T, 7) end-effector actions
+- `data/demo_{id}/obs/agentview_rgb`: (T, 256, 256, 3) top-down view
+- `data/demo_{id}/obs/eye_in_hand_rgb`: (T, 256, 256, 3) wrist camera
+- `data/demo_{id}/obs/ee_states`, `gripper_states`, `joint_states`: Robot state
+
+## Sample Usage (RLDS Conversion)
+
+You can use the `RoboMemArena_dataset_builder.py` script provided in the [GitHub repository](https://github.com/OpenHelix-Team/RoboMemArena) to convert the HDF5 data to RLDS (TFDS) format:
+
+```python
+import RoboMemArena_dataset_builder as b
+import tensorflow_datasets as tfds
+
+# Set the source dataset root and run the builder
+ds_builder = b.RoboMemArenaDataset(data_dir='/path/to/output')
+ds_builder.download_and_prepare()
+```
+
+## Citation
+
+```bibtex
+@article{robomemarena2025,
+  title   = {RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark},
+  author  = {Huashuo Lei and Wenxuan Song and Huarui Zhang and Jieyuan Pei and Jiayi Chen and Haodong Yan and Han Zhao and Pengxiang Ding and Zhipeng Zhang and Lida Huang and Donglin Wang and Yan Wang and Haoang Li},
+  journal = {arXiv preprint arXiv:2605.10921},
+  year    = {2026}
+}
+```