Datasets:

diffracting
/

egocentric-kitchen-sample

---

license: cc-by-4.0
pretty_name: Diffraction Egocentric Kitchen Capture Sample
configs:
- config_name: default
  data_files:
  - split: sample
    path: data/episodes.parquet
language:
- en
tags:
- video
- robotics
- egocentric
- rgbd
- physical-ai
- human-demonstration
- depth-estimation
size_categories:
- n<1K
---

# Diffraction Egocentric Kitchen Capture Sample

A small, inspectable sample of human kitchen manipulation captured with Stray Scanner on a LiDAR-equipped iPhone: native RGB, metric depth and confidence, per-frame camera calibration, device odometry, raw device IMU, and explicitly estimated hand/object annotations.

**Human observation sample.** License: cc-by-4.0. This sample contains 3 recordings totaling 167.85 seconds. It is an observation dataset for evaluating human-video pretraining, spatial perception, action understanding and motion-prediction workflows. It supplies no measured robot commands, force or tactile data, and demonstrates no robot-policy performance gain.

| Capture | Seconds | Native RGB frames | Instruction |
|---|---:|---:|---|
| onion-preparation | 75.73 | 4,543 | Prepare and peel the onion on the cutting board using hands and a kitchen knife. |
| pepper-slicing | 57.75 | 3,465 | Prepare and slice the bell pepper on the cutting board using a kitchen knife. |
| pepper-dicing | 34.37 | 2,062 | Dice the bell pepper strips on the cutting board using a kitchen knife. |

![Native RGB, metric depth and confidence](media/sample-overview.jpg)

## Download and try one episode

```bash

pip install huggingface_hub

hf download diffracting/egocentric-kitchen-sample --repo-type dataset --local-dir diffraction-sample

cd diffraction-sample

pip install -r requirements.txt

python observation_reader.py .

```

The download includes native RGB-D recordings and a portable reader. The Hugging Face dataset viewer uses smaller video previews; use `videos/` for native RGB training pixels.

```python

from observation_reader import ObservationDataset, camera_points

sample = ObservationDataset('.')

key = next(iter(sample.episodes))

frame = sample.frame(key, 100)

print(frame['rgb'].shape, frame['depth_m'].shape, frame['K_rgb'])

points = camera_points(frame)

hands = sample.annotations_near(key, 'hand_pose', frame['timestamp_s'], max_age_s=0.05)

```

Native videos are under `videos/`; smaller browser previews and episode metadata under `previews/`. Depth/confidence and original sensor CSVs are in `sensors/`. Annotation tables are standalone Parquet. [SCHEMA.md](SCHEMA.md) defines units, frames, clock mapping, confidence and missing-data behavior. `checksums.sha256` covers every packaged artifact.

## Collection and scope

The owner confirmed these Stray Scanner recordings are owned and may be used to prepare a training-data sample. This small collection shows closely related kitchen tasks; participant count, device model/app version and independent session identity were not supplied. All recordings belong to a conservative common session group. There is no claimed train/test benchmark or cross-person generalization.

The pipeline preserves native video bytes and joins camera/depth records by source frame ID, retaining both original clocks. Per-frame intrinsics are used for wrist lifting. Raw and temporally processed keypoints are separate. Instructions were prepared from video inspection; detailed action boundaries, success/failure and ground-truth contact annotations are not provided.

## Quality and limitations

Per-episode `quality/` reports expose channel coverage, null counts, provenance and sensor correspondence. These are engineering checks, not labeled accuracy benchmarks. ARKit camera pose is a device VIO estimate. Hand pose, palm orientation, object tracking and contact remain estimates; review their overlays and confidence/missingness before training. The low-resolution depth sensor can miss thin fingers, tool edges and occlusion boundaries. Fast cutting, bimanual occlusion and deformable vegetables are difficult cases.

Raw IMU acceleration units need capture-version verification; no silent unit conversion is applied. Device IMU is not wrist-motion sensing. See SCHEMA.md. An independent labeled evaluation and robot transfer experiment are future work, not measured results of this sample.

## Reproducibility and professional capture

The accompanying processing record identifies the source code and environment used. `QUALITY.md` summarizes observed coverage and limitations; `validation.json` records the portable-reader and file-integrity checks. Run `python validate_release.py .` after installing `datasets` alongside the reader requirements to repeat these checks. This sample is intended to start technical evaluation of task-specific professional capture: agree on target activities, environments, collection protocol, annotation requirements and measurable acceptance criteria before scaling a collection.

## References

[Stray Scanner source format](https://docs.strayrobots.io/apps/scanner/format.html), [Hugging Face video datasets](https://huggingface.co/docs/hub/datasets-video).

## Attribution

Please attribute **Diffraction, Egocentric Kitchen Capture Sample (2026)** and include the [dataset URL](https://huggingface.co/datasets/diffracting/egocentric-kitchen-sample) and the revision you used. The dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).