WorldArchive/mono-india-workplace-sample

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. 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.

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
• —Interactive explorer: data-explorer Space
• —Factory program: 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

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.

clips.parquet columns

segments.parquet columns

Full-pack JSONL fields (S3)

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

Browse previews in the Dataset Viewer

1. 1.Open the clips config in the Dataset Viewer.
2. 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. 3.For layer switching across all 9 clips, use the data-explorer Space.

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)

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)

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

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

Comparison with public egocentric corpora

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

Product tiers

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. Commercial production training and enterprise delivery are available under separate terms — contact shubham@worldarchive.co.

Citation

@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
• —Email: shubham@worldarchive.co · dheeraj@worldarchive.co
• —Web: worldarchive.co · factory.worldarchive.co