Datasets:
ArXiv:
License:
| 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} | |
| } | |
| ``` | |