| ---
|
| license: cc-by-4.0
|
| pretty_name: Diffraction Egocentric Kitchen Capture Sample
|
| configs:
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| - config_name: default
|
| data_files:
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| - split: sample
|
| path: data/episodes.parquet
|
| language:
|
| - en
|
| tags:
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| - video
|
| - robotics
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| - egocentric
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| - rgbd
|
| - physical-ai
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| - human-demonstration
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| - depth-estimation
|
| size_categories:
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| - 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. |
|
|
|
| 
|
|
|
| ## Download and try one episode
|
|
|
| ```bash
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| 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
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| 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)
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| 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/).
|
|
|
