Add observation.objects.cube [32] + rename observation_vector->observation.state (non-destructive superset)
f0a18d9 verified | license: mit | |
| pretty_name: block-stack | |
| tags: | |
| - world-models | |
| - robotics | |
| configs: | |
| - config_name: train | |
| data_files: train/data/**/*.parquet | |
| - config_name: val | |
| data_files: val/data/**/*.parquet | |
| - config_name: eval_purple_play | |
| data_files: eval_purple_play/data/**/*.parquet | |
| - config_name: eval_purple_stack | |
| data_files: eval_purple_stack/data/**/*.parquet | |
| # block-stack | |
| Recorded trajectories (no simulator) packaged as LeRobot splits for world-model training. | |
| Generated with [quickdraw](https://github.com/isaac-ward/quickdraw) (`data.processors`). | |
| ## Observation / action | |
| - **observation_vector** (17): the recorded state | |
| - **observation.images.scene_left** (144×192×3, video): recorded camera stream, stored as MP4, aligned | |
| 1:1 with the vector frames | |
| - **observation.images.scene_right** (144×192×3, video): recorded camera stream, stored as MP4, aligned | |
| 1:1 with the vector frames | |
| - **observation.images.gripper_right_bottom** (144×192×3, video): recorded camera stream, stored as MP4, aligned | |
| 1:1 with the vector frames | |
| - **observation.images.gripper_right_top** (144×192×3, video): recorded camera stream, stored as MP4, aligned | |
| 1:1 with the vector frames | |
| - **action** (5): the recorded actions, at 30 Hz | |
| ## Splits | |
| | split | episodes | steps/ep (mean) | frames | | |
| |---|---|---|---| | |
| | `train` | 43 | 4019 | 172835 | | |
| | `val` | 2 | 17400 | 34799 | | |
| | `eval_purple_play` | 5 | 1810 | 9048 | | |
| | `eval_purple_stack` | 6 | 887 | 5323 | | |
| Normalization statistics are computed on **train only** and applied to every split. | |
| ## How this dataset was produced | |
| See **[PROCESSING.md](PROCESSING.md)** for the full account: source streams and rates, exactly which fields were kept and dropped and why, the resampling and synchronisation procedure, the train/val split rule, and the verification that images align with state rows. | |
| --- | |
| ## Update 2026-09-24 — cube object-state features + `observation.state` rename | |
| **`observation_vector` was renamed to `observation.state`** (lerobot-canonical; matches our other datasets). | |
| Values are byte-identical to the old `observation_vector [17]`; only the field name changed. | |
| **New field: `observation.objects.cube` — shape `[32]`, float32.** Per-frame cube object-state, extracted by | |
| per-episode color segmentation (hue-peak palette discovery, so only colors actually present in a split are | |
| tracked — R/G/B for the standard task, R/B/P for the `eval_purple_*` splits). | |
| Layout: **4 cube slots (canonical order R, G, B, P) x 2 cameras (cam_scene=scene_right, cam_wrist=gripper_right_top) | |
| x 4 features**, flattened as [(cube j, cam c) for j in RGBP for c in (scene, wrist)] -> [f0..f3]. Per (cube,camera): | |
| | idx | feature | meaning | | |
| |----|---------|---------| | |
| | 0 | cx / W | centroid x / image width, in [0,1] | | |
| | 1 | cy / H | centroid y / image height, in [0,1] | | |
| | 2 | sqrt(area / TYP) | apparent linear extent (~ 1/depth proximity cue); TYP=120 px | | |
| | 3 | soft_vis | clip(area/TYP, 0, 1); 0 = not currently observed | | |
| **Occlusion:** if a cube is not detected but was seen earlier, its **position is held at last-seen** while | |
| soft_vis (and size) go to **0** -> `[cx, cy, 0, 0]` marks a held/occluded position. A cube color **absent** from a | |
| split (P in train/val; G in the purple splits) is **all-zeros** every frame. So soft_vis>0 = current; | |
| soft_vis=0 with nonzero position = carried through occlusion; all-zero = absent. | |
| Normalization stats for observation.objects.cube (normalization_stats.json + per-split meta/stats.json) are on | |
| the **train** split. Cameras/action/indices and the four observation.images.* video streams are unchanged. | |
| Extraction is deterministic classical CV (no learned detector, no labels). | |