README.md

---
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).