Add dataset card, paper link, and metadata

README.md

@@ -1,3 +1,46 @@
 ---
 license: apache-2.0
+task_categories:
+- robotics
 ---
+
+# RoboTwin-MeM
+
+RoboTwin-MeM is a diagnostic benchmark specifically designed to evaluate non-Markovian manipulation tasks with interactive visual evidence, introduced in the paper:
+
+**EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies**  
+[[Paper](https://huggingface.co/papers/2606.20092)] [[Project Page](https://ganlin-yang.github.io/EventVLA.github.io/)] [[Code](https://github.com/InternRobotics/EventVLA)]
+
+## Dataset Structure
+
+The dataset contains the following directory structure:
+
+```text
+RoboTwin-MeM/
+├── hdf5/          # HDF5 trajectories
+└── lerobot_2.1/   # LeRobot 2.1 training data
+```
+
+Both formats contain the following eight memory-dependent manipulation tasks:
+
+- `cover_blocks_hard`
+- `find_seal_and_seal_stamp`
+- `pick_objects_in_order`
+- `pick_the_unhidden_block`
+- `press_button_keyframe`
+- `put_back_block_hard`
+- `rearrange_blocks_hard`
+- `reproduce_route`
+
+## Citation
+
+If you find this dataset useful, please cite:
+
+```bibtex
+@article{yang2026eventvla,
+  title={EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies},
+  author={Yang, Ganlin and Tu, Zhangzheng and Yang, Yuqiang and Mao, Sitong and Dong, Junyi and Chen, Tianxing and Peng, Jiaqi and Xiong, Jing and Cao, Jiafei and Dai, Jifeng and others},
+  journal={arXiv preprint arXiv:2606.20092},
+  year={2026}
+}
+```