| --- |
| license: mit |
| task_categories: |
| - robotics |
| - video-classification |
| tags: |
| - robotics |
| - world-model |
| - video-prediction |
| - imitation-learning |
| - depth-estimation |
| - affordance |
| - libero |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # DeVA — LIBERO (processed) |
|
|
| Processed **LIBERO** data for [DeVA](https://github.com/Mq-Zhang1/deva), a robot video-world |
| model with affordance + depth physical guidance. Derived from |
| [Lifelong-Robot-Learning/LIBERO](https://github.com/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 |
|
|
| ```bash |
| hf download mengqz9/deva_libero --repo-type dataset --local-dir datasets/deva_libero |
| ``` |
|
|
| ## Layout |
|
|
| ``` |
| 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: |
|
|
| | | affordance | depth | |
| |---|---|---| |
| | key | `<view>` | `<view>` | |
| | dtype | `float16` | `float16` | |
| | shape | `[T, 128, 128]` | `[T, 128, 128]` | |
| | range | raw, un-normalized | `[0, 1]` | |
| | note | loader resizes to 128×128, then per-frame max-normalizes | per-view per-clip min-max applied at export | |
|
|
| `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: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
