| --- |
| 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) |
|
|
