Shipping · 2,310 validated hours

Household chores egocentric dataset

Laundry, tidying, cleaning and surface reset across whole homes — long-horizon, low-precision, high-navigation episodes.
Episodes
14,100
Participants
188
Capture sites
88
Sync ceiling
2.0 ms, hardware-triggered

What a frame contains

Every layer, on every clip in this set.

Rendered from the delivered schema rather than a marketing composite. Toggle layers to see what arrives with the footage.

bowl 0.97cutting_board 0.94mug 0.99bottle 0.91WRIST_RWRIST_LBODY 24 JOINTS · 3DHANDS 21 × 2 · 3D METRICGAZE · mugREC 00:04:18:163840×2160 · 60 FPSSYNC Δ 1.5 msEP_0117_KITCHEN_A / rig-04
00:04:18:16054/300 f

Synthetic frame, real schema. The sample pack contains the same fields for actual episodes.

A lived-in living room with a worn sofa, mugs on the coffee table and a laundry basket
Representative capture environment for this set. Rooms are recorded as found — clutter is signal, not something we tidy away before rolling.

Named skills

What the operators were told to do.

Coverage is specified per skill, not per hour. These are the skill lines currently filled in this environment.

  • Fold and sort laundry
  • Load, transfer, and unload a washer
  • Surface wipe and clutter reset
  • Vacuum with obstacle negotiation
  • Bed-making, bimanual sheet handling
  • Bin change and bag tie

Environments captured

  • Living space
  • Bedroom
  • Bathroom
  • Utility room
  • Stairs and hallway

Condition cells filled

Daylight
90%
Low light
55%
Multi-person
51%
Failure cases
22%

Known failure modes

What goes wrong in this environment.

Published because you will find these in the data within an afternoon, and it is cheaper for both of us if you find them in this list first.

  • Deformable objects dominate here, so instance masks are shipped per frame rather than propagated — annotation cost per hour is the highest in the catalog.
  • Bathroom capture excludes any episode with a person in frame other than the consenting operator.

What ships with every clip

Ten streams, one clock, one episode file.

Not an à la carte menu. Every validated hour we deliver carries the whole stack, in the schema below, whether you asked for depth or not.

StreamSpecDetail
Head camera3840 × 2160 · 60 fpsGlobal-shutter, 120° HFOV, rolling-shutter-free, H.265 + lossless keyframes
Wrist cameras × 21920 × 1080 · 60 fpsLeft + right, 100° HFOV, rigid mount, extrinsics re-solved per session
Depth848 × 480 · 30 fpsActive stereo, 0.3–4 m range, metric millimetres, per-frame confidence map
IMU200 Hz · 6-DoFAccel + gyro, bias-calibrated, hardware-timestamped on the same clock domain
Hand pose21 keypoints × 2 hands3D metric, per-joint visibility flag, contact events on grasp and release
Body pose24 joints3D, root-relative and world-frame, torso and forearm chains resolved
SegmentationInstance masksManipulated objects + target surfaces, tracked IDs across the episode
Action segmentsVerb + noun taxonomy97 verbs, 512 nouns, start/end to the frame, human-reviewed
FormatsRLDS · LeRobot · WebDatasetAlso HDF5, zarr, and .rrd for Rerun. Converters shipped as source.
LicenseCommercial · buyer-ownedPerpetual, irrevocable, model-weights-clean. Exclusivity available.

Colour dots map to the modality legend used in every chart on this site. Full field-level schema in the episode schema docs.

Data card

The card that ships with the batch.

Delivered as machine-readable JSON alongside the episodes, so provenance travels with the data instead of living in an email thread.

Dataset
Household chores egocentric dataset
Validated hours
2,310 h — passed sync and QA, shipped to at least one buyer
Episodes / participants / sites
14,100 / 188 / 88
Sync ceiling
Δ < 2.0 ms across all streams · median 1.12 ms · p99 1.94 ms
Consent
Written commercial release per participant, signed before capture begins
De-identification
Faces and licence plates blurred, audio scrubbed, un-blurrable clips discarded
Collection period
Rolling. Batch capture dates recorded per episode.
License
Perpetual, irrevocable, buyer-owned commercial. Exclusivity available.
Known limitations
Deformable objects dominate here, so instance masks are shipped per frame rather than propagated — annotation cost per hour is the highest in the catalog.
Formats
RLDS, LeRobot, WebDataset, HDF5, zarr, Rerun .rrd

Flagged frames are shipped rather than silently dropped. You decide whether to mask, down-weight, or exclude them.

Nearest public dataset

How this compares to Ego4D (household subset).

Where a public set is genuinely better at something, we say so. Where the blocker is the license rather than the quality, we say that too.

Ego4D (household subset)

Hours
~400 h
License
Commercial use permitted

Ego4D is commercially usable and broad, which is exactly why the gap is fitness: unstructured wandering footage, heavy motion blur, no hand pose, and no frame-accurate segment boundaries for long-horizon tasks. Ours is episode-structured with labelled task boundaries.

Firsthand — Household chores

Hours
2,310 h
License
Commercial, buyer-owned, perpetual

Captured against the named skill list above, hardware-synchronized under 2.0 ms, and shipped with depth, 3D hand pose, body pose, instance masks and action segments on every clip.

See the downstream result →

Pilot household chores against your spec.

Write the skill list with us, get fifty to a hundred validated hours in two weeks, and read the reject log before you commit to volume.