Add dataset card

README.md

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 ---
 license: mit
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+
+# eVTA₀ Datasets
+
+This repository hosts the training and evaluation data of the paper
+["Demonstration-Free Success-Probability Reward Learning for Generalist Robot
+Policies"](https://arxiv.org/abs/2609.33653):
+
+- **Rollout collection** (`data/libero/`): 2000 mixed-quality LIBERO policy
+  rollouts with soft failure targets (V-JEPA2 + DBSCAN + sigmoid
+  augmentation). 1600 trajectories are used for training eVTA₀
+  (890 success / 710 failure); the remaining 400 form the eval split,
+  used for evaluation.
+- **Trajectory split** (`train/libero/libero_trajectory_split.csv`):
+  per-rollout train / eval assignment with the success flag.
+- **Evaluation selections** (`eval/libero/`): the demonstration trajectories
+  used for VOC / VROC (`demonstrations/progress_trajectory_ids.csv`, 400
+  trajectories, 100 per suite) and the eval rollout manifest for
+  MSE / Kendall tau-a (`policy_rollouts/policy400_terminal_manifest.csv`,
+  400 rollouts; file paths relative to
+  `data/libero`).
+
+The VOC / VROC demonstrations are public LeRobot datasets, downloaded
+separately from the Hugging Face Hub: `lerobot/libero_spatial_image`,
+`lerobot/libero_object_image`, `lerobot/libero_goal_image`,
+`lerobot/libero_10_image` (`episode_index` in the selection CSV is the
+per-suite episode number).
+
+**MetaWorld:** TODO.
+
+## Data format
+
+The rollout collection is one HDF5 file per task with one `traj_*` group per
+rollout (`obs_main_images`, `obs_wrist_images`, `rewards`, `dones`,
+`actions`); `rewards[-1]` is the terminal target — 1.0 for successes and a
+soft value in (0, 1) for failures (see `data/libero/augmentation_report.json`
+and `../eVTA0/evta0/evta0/augment.py`).
+
+
+**Code:** https://github.com/duowuyms/eVTA0
+
+**Paper:** https://arxiv.org/abs/2609.33653
+
+**Author:** [Duo Wu](https://duowuyms.github.io/)
+
+## Citation
+
+If you find these datasets useful, please cite our paper:
+
+```bibtex
+@article{wu2026evta0,
+  title={Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies},
+  author={Wu, Duo and Wang, Haifeng and Lu, Rongwei and Wang, Jinghe and Xiong, Tianyi and Wang, Zhimin and Yu, Chao and Ma, Shuai and Wang, Zhi},
+  journal={arXiv preprint arXiv:2609.33653},
+  year={2026}
+}
+```