Datasets:

mengqz9
/

deva_yam

text stringclasses 3 values
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Pick up one bottle with one arm, and pick up another bottle with the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use one arm to grasp the marker on the table and handover it to the other arm.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.
Use arms to lift the pot.

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.