mengqz9/deva_yam

DeVA — YAM (processed)

Real bimanual-robot data collected on the YAM platform and processed for 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

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 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:

@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}
}