---
license: cc-by-nc-4.0
task_categories:
  - robotics
  - video-classification
  - object-detection
  - other
language:
  - en
tags:
  - egocentric
  - manipulation
  - physical-ai
  - vla
  - lerobot
  - hand-keypoints
  - india
  - robot-learning
  - world-archive
  - datasets
  - mlcroissant
pretty_name: World Archive Mono — India Workplace Egocentric Manipulation
size_categories:
  - n<1K
configs:
  - config_name: clips
    data_files: data/clips.parquet
  - config_name: segments
    data_files: data/segments.parquet
library_name: datasets
---

# World Archive Mono — India Workplace Egocentric Manipulation

**Ground-truth egocentric manipulation from the Indian real economy — robot-ready labels, not just video.**

A public evaluation sample from [World Archive](https://worldarchive.co). We run **managed, consent-first egocentric capture** at real Indian workplaces — factories, kitchens, repair bays, workshops — and ship a full annotation stack built for training and evaluating **manipulation policies, VLA models, and world models**.

| | |
|---|---|
| **Clips** | 9 (~48 min total) |
| **Action segments** | 218 (human-reviewed verb–noun phases) |
| **Median segment** | ~8s |
| **Annotation layers** | 8+ (segments, captions, hands, objects, contact, metadata, QA, consent) |
| **CI QA pass** | 9/9 clips |
| **LeRobot mirror** | [`WorldArchive/mono-india-workplace-lerobot`](https://huggingface.co/datasets/WorldArchive/mono-india-workplace-lerobot) — 9 episodes, 46,436 frames @ 15fps |
| **Full pack** | [S3 sample index](https://ggn-egocentric-data-sample.s3.ap-south-1.amazonaws.com/sample_data_june/index.html) (~19 GB, no login) |
| **Live explorer** | [HF Space](https://huggingface.co/spaces/WorldArchive/data-explorer) |
| **Collection** | [Physical AI India](https://huggingface.co/collections/WorldArchive/physical-ai-india) |

## Dataset Description

Nine egocentric video clips of real manual work in Indian workplaces: factory packaging, industrial sewing, heat-shrink batching, garment ironing, commercial catering, cane weaving, car detailing, auto-body primer/painting, and denting/filing. Each clip ships with temporal action segments, per-frame hand keypoints, object bounding boxes, hand–object contact samples, metadata, QA flags, and commercial AI-training consent documentation.

**Source:** Managed partner-site capture (not contributor apps). Head-mounted smartphone rigs operated by workers under documented consent.

**Geography:** India — factory floors, restaurants, roadside shops, showrooms, and repair bays across the real economy.

**Intended use:** Training and evaluating vision-language-action models, imitation learning, hand-object interaction research, egocentric video understanding, and physical-AI benchmarks in industrial and service settings.

**Out of scope:** Surveillance, worker performance scoring, biometric identification, or any use that re-identifies participants.

### Verticals

`shuttle-tube packaging` · `industrial sewing` · `heat-gun batching` · `garment ironing & packing` · `commercial catering` · `cane weaving` · `car detailing` · `primer & painting` · `denting & filing`

### Related assets

- **LeRobot mirror:** [mono-india-workplace-lerobot](https://huggingface.co/datasets/WorldArchive/mono-india-workplace-lerobot)
- **Interactive explorer:** [data-explorer Space](https://huggingface.co/spaces/WorldArchive/data-explorer)
- **Factory program:** [factory.worldarchive.co](https://factory.worldarchive.co)
- **Technical memo:** `docs/buyer-technical-memo.md` in this repo
- **Paper (forthcoming):** placeholder — cite this dataset card until preprint is live

### Technical essays

Long-form notes on annotation density, capture ops, and trainable signal:

- **[The density advantage: labels per minute that actually train policies](https://worldarchive.co/blog/annotation-density)** — also in `blog/01-annotation-density.md` in this repo
- **[Beyond the Monocular Plateau: How DataOps Wins the Next Quarter](https://worldarchive.co/blog/future-of-physical-ai-dataops)** — also in `blog/04-future-of-physical-ai-dataops.md` in this repo
- Essay index: [worldarchive.co/blog](https://worldarchive.co/blog)

## Dataset Structure

### Repository layout

```
mono-india-workplace-sample/
├── README.md
├── DATACARD.md
├── DELIVERY_OVERVIEW.md
├── data/
│   ├── clips.parquet          # 9 rows — one per clip
│   ├── segments.parquet       # 218 rows — verb–noun phases
│   └── pack_summary.json
├── clips_preview/             # 6s MP4 previews (plain / skeleton / boxes)
│   └── sample_XX_*/{plain,skeleton,boxes}.mp4
├── schema/                    # Field dictionaries
│   ├── annotation_schema.md
│   ├── action_taxonomy.md
│   ├── object_boxes_schema.md
│   └── ...
└── docs/
    └── buyer-technical-memo.md
```

Full MP4 + JSONL annotations (~19 GB) live on [S3](https://ggn-egocentric-data-sample.s3.ap-south-1.amazonaws.com/sample_data_june/index.html).

### `clips.parquet` columns

| Column | Type | Description |
|--------|------|-------------|
| `clip_id` | string | Stem, e.g. `sample_01_shuttle_tube_packaging` |
| `title` | string | Human-readable task name |
| `environment` | string | `factory`, `restaurant`, `repair shop`, etc. |
| `device` | string | Capture smartphone model |
| `session_id` | string | Session identifier |
| `video_file` | string | MP4 filename |
| `duration_sec` | float | Clip length |
| `fps` | float | Native frame rate |
| `resolution` | string | e.g. `1920x1080` |
| `mount_type` | string | Headband mount |
| `segment_count` | int | Action segments in clip |
| `hands_visible_pct` | float | Fraction of frames with visible hands |
| `two_hands_pct` | float | Fraction with two hands visible |
| `manipulation_density_pct` | float | Derived manipulation score |
| `qa_pass` | bool | CI QA pass flag |
| `consent_signed` | bool | Commercial AI consent on file |
| `s3_video_url` | string | Full-resolution MP4 on S3 |
| `s3_overlay_url` | string | Hand skeleton overlay MP4 |
| `s3_boxes_preview_url` | string | Object-box preview MP4 |
| `s3_metadata_url` | string | Per-clip metadata JSON |
| `hf_preview_plain_url` | string | 6s plain preview on HF |
| `hf_preview_skeleton_url` | string | 6s skeleton preview on HF |
| `hf_preview_boxes_url` | string | 6s boxes preview on HF |

### `segments.parquet` columns

| Column | Type | Description |
|--------|------|-------------|
| `clip_id` | string | Clip stem |
| `video` | string | MP4 filename |
| `start_sec` | float | Segment start (clip-relative) |
| `end_sec` | float | Segment end |
| `duration_sec` | float | Segment length |
| `action` | string | Verb (human-reviewed) |
| `object` | string | Noun / manipulated object |
| `task` | string | Combined task label |
| `notes` | string | Operator notes |

### Full-pack JSONL fields (S3)

| File pattern | Key fields |
|--------------|------------|
| `annotations/action_segments.jsonl` | `video`, `start_sec`, `end_sec`, `action`, `object`, `task`, `notes` |
| `annotations/*_hand_keypoints.jsonl` | `frame_idx`, `timestamp_sec`, `hands[]` with 21 landmarks (`x`,`y`,`z`) |
| `annotations/*_object_boxes.jsonl` | `frame_idx`, `boxes[]` with `bbox`, `label`, `track_id`, `source` |
| `annotations/*_hand_boxes.jsonl` | Per-hand axis-aligned boxes |
| `annotations/*_hand_object_contact.jsonl` | Derived contact events |
| `annotations/*_captions.jsonl` | Natural-language clip summary |
| `metadata/*.json` | Device, consent, QA flags, manipulator stats |

Label provenance is explicit: segments & captions are **human**; keypoints & boxes are **model-generated** with `source` fields.

## Browse previews in the Dataset Viewer

1. Open the **clips** config in the [Dataset Viewer](https://huggingface.co/datasets/WorldArchive/mono-india-workplace-sample/viewer/clips).
2. Click `hf_preview_plain_url`, `hf_preview_skeleton_url`, or `hf_preview_boxes_url` on any row to play a 6s inline preview.
3. For layer switching across all 9 clips, use the [data-explorer Space](https://huggingface.co/spaces/WorldArchive/data-explorer).

Preview files live under `clips_preview/{clip_id}/{plain,skeleton,boxes}.mp4`.

## Supported Tasks

- Egocentric action recognition (verb–noun segments)
- Temporal action segmentation and phase detection
- Hand pose estimation (21-joint 2D landmarks)
- Hand–object interaction and contact modeling
- Object detection and tracking in manipulation scenes
- Vision-language-action (VLA) pretraining on human video
- Imitation learning from egocentric demonstrations
- Robot policy evaluation on out-of-distribution industrial tasks
- Cross-embodiment transfer (human ego → robot arms)
- World-model training with action-conditioned video
- Manipulation density and hand-visibility benchmarking
- Geographic / cultural distribution analysis (India real economy)
- Consent-aware dataset auditing for commercial AI training
- LeRobot-format policy learning (via mirror dataset)
- Physical-AI benchmark design for factory and service labor
- Tool-use and dexterous manipulation in unstructured workshops

## Usage

### Metadata index (Hugging Face `datasets`)

```python
from datasets import load_dataset

clips = load_dataset(
    "WorldArchive/mono-india-workplace-sample",
    "clips",
    split="train",
)
segments = load_dataset(
    "WorldArchive/mono-india-workplace-sample",
    "segments",
    split="train",
)
print(clips[0]["title"], clips[0]["hf_preview_plain_url"])
print(segments[0]["action"], segments[0]["object"])
```

### Robot-ready frames (LeRobot)

```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("WorldArchive/mono-india-workplace-lerobot")
print(ds.num_episodes, ds.num_frames, ds.fps)
sample = ds[0]  # observation.images.ego, observation.state (126-d), task
```

### Full videos + dense JSONL

```bash
aws s3 sync s3://ggn-egocentric-data-sample/sample_data_june ./Master_Sample_v1 --no-sign-request
```

## Comparison with public egocentric corpora

| | Ego4D | Build AI Egocentric-100K | World Archive Mono |
|---|---|---|---|
| **Scale** | ~3,670 hrs daily-life ego | ~100k hrs factory (China) | 9 clips, ~48 min (evaluation sample) |
| **Setting** | Western-heavy daily life (cooking, social, errands) | Chinese factory floors | Indian real economy (factory, catering, repair, craft) |
| **Annotations** | Partial (narrations, AV, some hands/objects) | Minimal public labels; raw video + intrinsics | 218 human verb–noun segments; hands, boxes, contact, QA |
| **Geography** | US/EU/Singapore-heavy | China | India |
| **License / access** | Research license (FAIR) | Gated; commercial terms | CC BY-NC 4.0 eval sample; commercial training license available |
| **Robot format** | Custom JSON exports | Raw video | Native [LeRobot](https://huggingface.co/datasets/WorldArchive/mono-india-workplace-lerobot) mirror |
| **Capture model** | Crowd + research partners | Managed factory deployment | Managed partner sites, consent-first |
| **Consent for commercial AI** | Research-oriented | Enterprise (gated) | Documented commercial AI-training consent |

## Why this matters for VLA / robot learning

Generalization in manipulation is bottlenecked by **distribution diversity**. Most public ego data skews Western, kitchen/household, or lab teleop. World Archive contributes **real industrial and service-economy manipulation** with the spatial and temporal labels policies consume (hand pose, contact, verb–noun, object grounding).

## Capture & QA pipeline

1. **Capture** — managed partner sites, headband ego rig
2. **Consent** — commercial AI-training consent before delivery
3. **Anonymize** — PII/face review; audio stripped
4. **Annotate** — segments, captions, hands, objects, contact
5. **Manual QA** — human verification before promote
6. **Deliver** — MP4 + JSONL + schema docs

## Product tiers

| Tier | Description |
|------|-------------|
| **Mono Clear** (this repo) | Headband smartphone ego + full annotation stack |
| **Pro Multi-Sensor** (pilot) | Ego + wrist cam + IMU + depth + exo, time-synced — [contact us](mailto:shubham@worldarchive.co) |

## Limitations

- **Sample size** — 9 clips for evaluation, not pretraining at scale.
- **Geography** — India workplaces only; not globally representative.
- **Monocular** — no wrist camera, depth, or IMU in this sample (see Pro tier).
- **Object boxes** — sampled ~1 Hz, not dense per-frame.
- **Hand keypoints** — estimated 2D (MediaPipe), not metric 3D ground truth.
- **License** — CC BY-NC 4.0 for evaluation; production commercial training requires a separate license.

## License

This evaluation sample is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). Commercial production training and enterprise delivery are available under separate terms — contact [shubham@worldarchive.co](mailto:shubham@worldarchive.co).

## Citation

```bibtex
@dataset{worldarchive_mono_india_2026,
  title        = {World Archive Mono: India Workplace Egocentric Manipulation Sample},
  author       = {World Archive / GGN},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/WorldArchive/mono-india-workplace-sample}},
  note         = {9 clips, 218 action segments, LeRobot mirror available}
}
```

## Contact

- Book a call: [Calendly](https://calendly.com/algorithmsdheeraj/30min)
- Email: [shubham@worldarchive.co](mailto:shubham@worldarchive.co) · dheeraj@worldarchive.co
- Web: [worldarchive.co](https://worldarchive.co) · [factory.worldarchive.co](https://factory.worldarchive.co)