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README.md

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+---
+license: mit
+task_categories:
+  - robotics
+  - video-classification
+tags:
+  - robotics
+  - world-model
+  - video-prediction
+  - imitation-learning
+  - depth-estimation
+  - affordance
+  - robocasa
+size_categories:
+  - 1K<n<10K
+---
+
+# DeVA — RoboCasa (processed)
+
+Processed **RoboCasa** data for [DeVA](https://github.com/Mq-Zhang1/deva), a robot
+video-world model with affordance + depth physical guidance. Derived from
+[robocasa/robocasa](https://github.com/robocasa/robocasa) —
+**please cite the original benchmark as well**.
+
+| | |
+|---|---|
+| Episodes | 1,199 |
+| Frames | 316,995 |
+| Video | 448 × 448, 16 fps (3 views on a 2×2 grid) |
+| Views | `agentview_left`, `agentview_right`, `eye_in_hand` |
+| Action dim | 7 (absolute) |
+| Proprio state | not included |
+| Size | 56 GB |
+
+## Download
+
+```bash
+hf download mengqz9/deva_robocasa --repo-type dataset --local-dir datasets/deva_robocasa
+```
+
+## Layout
+
+```
+deva_robocasa/
+├── 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` — the
+bottom-right cell is empty:
+
+```
+[[agentview_left, agentview_right],
+ [eye_in_hand,    null           ]]
+```
+
+## 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, 224, 224]` | `[T, 224, 224]` |
+| 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**. No percentile clip is applied: the range is set by the
+farthest pixel, so agentview content occupies roughly the lower half of `[0, 1]` and
+never saturates.
+
+**Affordance source** — simulator oracle contacts rendered as Gaussian heatmaps.
+
+## Citation
+
+If you use this data, please cite DeVA and RoboCasa:
+
+```bibtex
+@inproceedings{robocasa2024,
+  title     = {RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots},
+  author    = {Nasiriany, Soroush and Maddukuri, Abhiram and Zhang, Lance and
+               Parikh, Adeet and Lo, Aaron and Joshi, Abhishek and
+               Mandlekar, Ajay and Zhu, Yuke},
+  booktitle = {Robotics: Science and Systems (RSS)},
+  year      = {2024}
+}
+```