mengqz9/deva_libero
DeVA — LIBERO (processed) Processed LIBERO data for DeVA, a robot video-world model with affordance + depth physical guidance. Derived from Lifelong-Robot-Learning/LIBERO — please cite the original benchmark as well. Episodes 2,000 Frames 338,575 Video 432 × 768, 16 fps (2 views tiled side by side) Views agentview, eye_in_hand Action dim 7 (absolute) Proprio state not included Size 9.6 GB Download hf download mengqz9/deva_libero… See the full description on the dataset page: https://huggingface.co/datasets/mengqz9/deva_libero.
DeVA — LIBERO (processed)
Processed LIBERO data for DeVA, a robot video-world model with affordance + depth physical guidance. Derived from Lifelong-Robot-Learning/LIBERO — please cite the original benchmark as well.
Download
hf download mengqz9/deva_libero --repo-type dataset --local-dir datasets/deva_liberoLayout
deva_libero/
├── dataset_info.json # view names, tile grid, affordance/depth encodings
├── videos/ # <episode>.mp4 tiled multi-view RGB
├── metas/ # <episode>.txt language instruction
├── t5_xxl/ # <episode>.pickle precomputed T5-XXL embedding [n_tok, 1024]
├── action.json # {episode: [[a_0..a_6], ...]} len == video frames
├── data_mapping.json # episode -> source task
├── norm_stats.json # action normalization statistics
├── affordance/ # <episode>.npz physical guidance (optional at train time)
└── depth/ # <episode>.npz physical guidance (optional at train time)videos/ tiles the views on the grid given by dataset_info.json::tile:
[[agentview, eye_in_hand]]Affordance / depth .npz
Both are keyed by canonical view name (never by tile position), one array per view:
T equals the episode's video frame count, so aux arrays index by absolute frame. The validity mask is derived from the tile grid (null cells are invalid) and is not stored.
Depth source — DepthAnything (ViT-L) disparity, converted as depth = 1 / disparity; higher value = farther. The far tail is clipped at the 98th percentile before per-view min-max, so ~2% of pixels saturate at 1.0.
Affordance source — simulator oracle contacts rendered as Gaussian heatmaps.
Citation
If you use this data, please cite DeVA and LIBERO:
@article{zhang2026deva,
title = {{DeVA}: Decoupled Video-Action Model with physical guidance for robot policy learning},
author = {Zhang, Mengqi and Khose, Sahil and Kareer, Simar and
Song, Yuchen and Jain, Unnat and Hoffman, Judy},
journal = {arXiv preprint arXiv:2607.24159},
year = {2026}
}
@article{liu2023libero,
title = {LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning},
author = {Liu, Bo and Zhu, Yifeng and Gao, Chongkai and Feng, Yihao and
Liu, Qiang and Zhu, Yuke and Stone, Peter},
journal = {NeurIPS Datasets and Benchmarks},
year = {2023}
}