zeno-labs/egostation-smartphone-raw-v1

EgoStation Smartphone Raw Catalog v1

Pre-processing catalog of smartphone egocentric video recordings. Most episodes are exposed as metadata only; a small set of representative sample episodes are shipped with full raw video + derived data so partners can inspect data quality before signing up for the full corpus.

• Built: 2026-05-12T19:05Z
• Total episodes: 5962
• Total recorded time: ~693.9 hours
• Format: Apache Parquet (catalog) + raw media for sample episodes
• Maintained by: ZenO Labs — https://zen-o.xyz

Data exposure policy

There are two tiers of access in this repo:

1. Sample episodes (samples/<domain>/<upload_id>/ — full data)

For a curated subset, the actual files are shipped inside this HF repo:

File	Source	Purpose
video.mp4 (or .mov)	ZenO Core / R2 original	Raw egocentric capture
pose_keypoints.json	MediaPipe Hands (Zeno pipeline)	Per-frame 2D hand keypoints (21 points × 2 hands)
depth_overlay.mp4	Zeno depth pipeline	Colorized relative depth visualization
meta.json	this catalog	Episode metadata snapshot

These are the only files in this repo that can be downloaded directly. Mission domain (cooking, kitchen-cleanup, …) decides the sub-folder.

2. All other episodes (~5946+ — metadata only)

The remaining episodes are listed in catalog.parquet with full metadata, but the actual media files are not in this repo. Each row's modality flags (hand_pose, depth, hand_world, …) show what can be delivered on request, packaged in LeRobot v2.1 format.

To obtain raw video, derived data, or a full LeRobot bundle for any upload_id outside the samples/ folder, contact support@zen-o.xyz with the upload_id list and the modalities you need.

Why is no derived data available yet?

These recordings are smartphone first-person videos captured by ZenO contributors. They are queued for processing once the storage infrastructure migration completes. After processing, episodes will graduate to the main egostation-catalog-v1 catalog with full LeRobot v2.1 data layout.

Per-episode availability flags

Each row in catalog.parquet carries a status string per modality:

Field	Meaning
head_trajectory	not-supported — smartphone capture lacks synchronized inertial data, so mono-inertial SLAM cannot recover 6DoF camera pose.
hand_pose	available — derivable on request (2D keypoints, both hands, 21 points each).
depth	available — relative depth maps derivable on request.
hand_world	available — 3D hand keypoints in camera frame derivable on request (depends on depth).
action_segment	available — manual annotation possible on request.

After processing, the value updates to completed and the episode is moved to the main catalog.

catalog.parquet schema

Field	Type	Description
episode_id	string	ep_NNNNNN, unique within this repo
upload_id	string	ZenO Core upload identifier — abstract handle (does not change across storage migrations)
source	string	smartphone
mission_name	string	Task category (e.g. Cooking, Washing Dishes, Tidy Up Shoes)
mission_group	string	Higher-level grouping (Mission 5, Mission 7, …)
mission_id	int	Internal ZenO Core mission identifier
file_name	string	Original device filename
file_size_mb	int	File size in megabytes
file_type	string	"Video"
duration_s	float	Episode length in seconds
width, height	int	Original video resolution
uploaded_at	timestamp (UTC, ISO 8601)	When the contributor uploaded
catalog_tier	string	private (raw catalog — metadata only)
head_trajectory, hand_pose, depth, hand_world, action_segment	string	Modality availability status
is_processed	bool	false for all rows in this raw catalog

Mission distribution

• Washing Dishes: 1216
• Cooking: 1131
• Wipe Objects and Furniture: 863
• Gripping a Door Handle: 629
• Kitchen Cleanup: 531
• Laundry Folding Challenge: 459
• Tidy Up Challenge: 260
• Home Cleaning Task: 219
• Dishwashing Task: 217
• Tidy Up Your Room: 188
• Laundry Task: 182
• Tidy Up Shoes: 67

Sample episodes (real data)

16 representative episodes with raw video + 2D hand keypoints, organized by mission domain.

egostation-smartphone-raw-v1/
└── samples/
    ├── cooking/
    │   ├── 89c5487e…/
    │   │       ├── video.mp4    (843 MB, 731s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── e6966629…/
    │   │       ├── video.mp4    (718 MB, 625s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 630c7405…/
    │   │       ├── video.mp4    (690 MB, 1046s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   └── 72700d9c…/
    │           ├── video.mp4    (981 MB, 1003s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── kitchen-cleanup/
    │   └── e0797265…/
    │           ├── video.mp4    (353 MB, 445s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── laundry-folding-challenge/
    │   ├── 8e3cfad8…/
    │   │       ├── video.mp4    (567 MB, 587s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 38c6b391…/
    │   │       ├── video.mp4    (592 MB, 414s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 00c5a81c…/
    │   │       ├── video.mp4    (925 MB, 946s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   └── dc36f9b0…/
    │           ├── video.mp4    (653 MB, 372s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── tidy-up-shoes/
    │   └── d311a32e…/
    │           ├── video.mp4    (902 MB, 923s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    ├── washing-dishes/
    │   ├── 7c98ebfb…/
    │   │       ├── video.mp4    (638 MB, 802s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 50b9fc4b…/
    │   │       ├── video.mp4    (943 MB, 964s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 61e65f9c…/
    │   │       ├── video.mp4    (704 MB, 619s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   ├── 71fcad78…/
    │   │       ├── video.mp4    (618 MB, 718s)
    │   │       ├── pose_keypoints.json
    │   │       ├── depth_overlay.mp4
    │   │       └── meta.json
    │   └── dfea9e2f…/
    │           ├── video.mp4    (587 MB, 695s)
    │           ├── pose_keypoints.json
    │           ├── depth_overlay.mp4
    │           └── meta.json
    └── wipe-objects-and-furniture/
        └── 7de39f3a…/
                ├── video.mp4    (910 MB, 930s)
                ├── pose_keypoints.json
                ├── depth_overlay.mp4
                └── meta.json

Sample list

Mission	Episode	Duration	Video	Hand pose	Depth
Laundry Folding Challenge	8e3cfad8…	9:46	567 MB	✓	✓
Cooking	89c5487e…	12:10	843 MB	✓	✓
Laundry Folding Challenge	38c6b391…	6:54	592 MB	✓	✓
Washing Dishes	7c98ebfb…	13:22	638 MB	✓	✓
Kitchen Cleanup	e0797265…	7:24	353 MB	✓	✓
Laundry Folding Challenge	00c5a81c…	15:45	925 MB	✓	✓
Washing Dishes	50b9fc4b…	16:03	943 MB	✓	✓
Cooking	e6966629…	10:24	718 MB	✓	✓
Washing Dishes	61e65f9c…	10:18	704 MB	✓	✓
Cooking	630c7405…	17:25	690 MB	✓	✓
Washing Dishes	71fcad78…	11:58	618 MB	✓	✓
Wipe Objects and Furniture	7de39f3a…	15:30	910 MB	✓	✓
Washing Dishes	dfea9e2f…	11:35	587 MB	✓	✓
Tidy Up Shoes	d311a32e…	15:22	902 MB	✓	✓
Cooking	72700d9c…	16:42	981 MB	✓	✓
Laundry Folding Challenge	dc36f9b0…	6:12	653 MB	✓	✓

Sample episodes are full quality — for the rest of the catalog, raw videos are available via R2 pre-signed URL under signed agreement.

Usage

from datasets import load_dataset

catalog = load_dataset("zeno-labs/egostation-smartphone-raw-v1", "catalog", split="train")
df = catalog.to_pandas()
print(df.head())

# Filter to a domain
cooking = df[df["mission_name"] == "Cooking"]
print(f"Cooking episodes: {len(cooking)}, total {cooking['duration_s'].sum()/3600:.1f}h")

Future structure (after processing)

Each episode, once processed, will be packaged in the LeRobot v2.1 format under the main catalog:

egostation-catalog-v1/
└── smartphone/
    └── <mission_name>/
        ├── data/chunk-NNN/episode_NNNNNN.parquet
        ├── videos/chunk-NNN/
        │   ├── observation.images.head/episode_NNNNNN.mp4
        │   └── observation.images.depth/episode_NNNNNN.mp4
        └── meta/
            ├── info.json
            ├── episodes.jsonl
            └── tasks.jsonl

Access to raw videos

Metadata in this catalog is freely browsable. For raw video access (needed for downstream processing or your own pipeline), contact support@zen-o.xyz with your upload_id list.

Licensing

Catalog metadata: CC-BY-NC 4.0. Raw videos: commercial license required.

Contact

• Data access & partnerships: support@zen-o.xyz
• Technical issues: support@zen-o.xyz
• Website: https://zen-o.xyz
• Pipeline (ZenO Studio): https://studio.zen-o.xyz