Datasets:

diffracting
/

egocentric-maintenance-sample

---

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

# Diffraction Egocentric Maintenance Sample

![Diffraction maintenance capture](preview.jpg)

Chest-mounted iPhone video of hands-on appliance maintenance: tape removal, brushing, panel handling and wiping recessed surfaces. 6 curated excerpts complement [Diffraction's RGB-D kitchen sample](https://huggingface.co/datasets/diffracting/egocentric-kitchen-sample) with a different task domain.

This is human RGB observation data for evaluating video-language, temporal action understanding and hand/object interaction workflows. Depth, metric camera calibration/pose, IMU, robot commands and execution-success labels are unavailable. Optional hand/object annotations are model estimates with no independently measured accuracy.

| Excerpt | Seconds | RGB frames | Instruction |
|---|---:|---:|---|
| remove-tape | 28.98 | 870 | Remove pieces of adhesive tape from the air conditioner front housing. |
| brush-front-grille | 29.95 | 899 | Brush the air conditioner front grille while stabilizing the housing. |
| brush-housing | 29.98 | 900 | Prepare the brush and scrub the exposed upper housing of the air conditioner. |
| brush-panel-channels | 29.98 | 900 | Hold the detached front panel and brush along its narrow channels. |
| wipe-panel | 29.98 | 900 | Wipe the detached panel surfaces while stabilizing the panel with the other hand. |
| wipe-panel-corners | 27.95 | 839 | Wipe the recessed corners and channels of the detached panel with a paper towel. |

**176.84 seconds · 5,308 RGB frames · one session · 13 approximate action intervals.**

## Download and use

```bash

pip install huggingface_hub

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

cd diffraction-maintenance

pip install -r requirements.txt

python observation_reader.py .

```

```python

from observation_reader import ObservationDataset

sample = ObservationDataset('.')

key = next(iter(sample.episodes))

frame = sample.frame(key, 100)

print(frame['rgb'].shape, frame['timestamp_s'], frame['source_timestamp_s'])

assert frame['depth_m'] is None

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

```

## Capture and processing

The source files identify an iPhone 15 Pro recording 1920 x 1080 HLG HDR video at approximately 30 fps. The owner described a chest-mounted camera setup and authorized the public sample. Full original MOV files are preserved locally with SHA-256 provenance. Public RGB clips are documented 8-bit SDR/H.264 excerpts with original presentation-time spacing and one-to-one source frame mapping. Audio and source-container metadata are omitted. Smaller browser videos are previews; use `videos/` for the training representation.

All excerpts appear to belong to the same maintenance session. They are one technical sample, not six independent trials or a train/test benchmark. No claim of a completed maintenance procedure or verified appliance operation is made.

## Quality and intended use

[QUALITY.md](QUALITY.md) reports review coverage and known failure modes; [validation.json](validation.json) records the executable release checks. [SCHEMA.md](SCHEMA.md) defines clocks, transforms, units and missing-data behavior. Per-excerpt `quality/` reports provide coverage and missingness; review overlays expose optional estimated annotations. Occlusion, close tools, deformable paper and repetitive cleaning are difficult cases. A detection score is not a calibrated error probability. Handedness conflicts become unknown. Any language intervals are explicitly curator annotations with a stated review method.

A downstream lab should evaluate relevant labels and model outcomes before scaling training. This sample demonstrates capture and delivery structure; no robot-policy performance gain or independent annotation-accuracy result is claimed.

## Verify and evaluate

```bash

pip install -r requirements-validation.txt

# Install FFmpeg/ffprobe on PATH, then:

python validate_release.py .

```

`checksums.sha256` covers every release artifact. `processing_record.json` records processing tools, source/derivative lineage and the distinction between batch execution and final assembly code. `source_index.json` records selection windows and the owner authorization record. The public preview catalog can also be loaded with `datasets.load_dataset("diffracting/egocentric-maintenance-sample", split="sample")`; install the video decoder dependencies appropriate to your environment, or use `datasets.Video(decode=False)` to retrieve embedded preview bytes.

## Work with Diffraction

This sample is a starting point for task-specific professional egocentric capture. A pilot can define the activities, workplaces, camera setup, review protocol and measurable delivery criteria with the receiving lab. Contact Diffraction through the [organization page](https://huggingface.co/diffracting) or start a [dataset discussion](https://huggingface.co/datasets/diffracting/egocentric-maintenance-sample/discussions).

## License and attribution

CC BY 4.0 permits reuse under its attribution terms. Attribute Diffraction, Egocentric Maintenance Sample (2026), this dataset URL and the revision used. See [LICENSE](LICENSE).