File size: 3,304 Bytes
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license: mit
task_categories:
- robotics
- video-classification
tags:
- robotics
- world-model
- video-prediction
- imitation-learning
- depth-estimation
- affordance
- bimanual
- real-robot
size_categories:
- n<1K
---
# DeVA — YAM (processed)
Real bimanual-robot data collected on the [YAM](https://www.i2rt.com) platform and
processed for [DeVA](https://github.com/Mq-Zhang1/deva), a robot video-world model
with affordance + depth physical guidance.
| | |
|---|---|
| Episodes | 90 |
| Frames | 30,749 |
| Video | 480 × 1920, 30 fps (3 views tiled side by side) |
| Views | `left_wrist`, `head`, `right_wrist` |
| Action dim | 14 (bimanual, delta actions at train time) |
| Proprio state | included (`state.json`) |
| Size | 19 GB |
## Download
```bash
hf download mengqz9/deva_yam --repo-type dataset --local-dir datasets/deva_yam
```
## Layout
```
deva_yam/
├── 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_13], ...]} len == video frames
├── state.json # {episode: [[s_0..s_13], ...]} proprioceptive state
├── norm_stats.json # action normalization statistics (quantile)
├── 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`:
```
[[left_wrist, head, right_wrist]]
```
The gripper channels of `action.json` are **binarized** (open/closed), which is
intentional for this dataset.
## 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, 480, 640]` |
| 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**. Clipped at export (percentile not recorded); ~5% of pixels
saturate at 1.0.
**Affordance source** — [UAD](https://unsup-affordance.github.io/) pseudo-labels rendered as
Gaussian heatmaps. Note that the real-robot affordance footprint is noticeably broader
than the simulator oracle labels used for LIBERO / RoboCasa.
## Citation
If you use this data, please cite DeVA:
```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}
}
```
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