---
license: mit
---

# 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}
}
```