Datasets:
Download README.md from notmuch2/eVTA0_datasets: direct link, hf CLI and curl.
- Browser
- Download file 2.24 kB
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https://huggingface.co/datasets/notmuch2/eVTA0_datasets/resolve/main/README.md
- Command line
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hf download hf://datasets/notmuch2/eVTA0_datasets/README.md
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curl -L -o README.md https://huggingface.co/datasets/notmuch2/eVTA0_datasets/resolve/main/README.md
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":
- 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 todata/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
Citation
If you find these datasets useful, please cite our paper:
@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}
}