Shipping · 640 validated hours
Automotive service bay egocentric dataset
- Episodes
- 3,980
- Participants
- 71
- Capture sites
- 14
- Sync ceiling
- 2.0 ms, hardware-triggered
Related: Automotive service robotics training data
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.
Synthetic frame, real schema. The sample pack contains the same fields for actual episodes.
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.
- Torque wrench to spec on a wheel
- Fluid drain and fill
- Trim and clip removal without damage
- Diagnostic connector seat and read
- Filter swap in a constrained cavity
Environments captured
- Lift bay
- Tyre bay
- Engine bay work
- Under-vehicle on lift
Condition cells filled
- Mixed light
- 64%
- Low light
- 41% — under-vehicle work with head torch
- Cluttered
- 83% — the most cluttered cells we hold
- Failure cases
- 31% — cross-threads, dropped fasteners, slips
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.
- Under-vehicle capture is IR-hostile: painted and oily surfaces both absorb the projector pattern. Those episodes carry a depth-quality flag.
- Licence plates and VIN plates are blurred at ingest without exception.
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.
| Stream | Spec | Detail |
|---|---|---|
| Head camera | 3840 × 2160 · 60 fps | Global-shutter, 120° HFOV, rolling-shutter-free, H.265 + lossless keyframes |
| Wrist cameras × 2 | 1920 × 1080 · 60 fps | Left + right, 100° HFOV, rigid mount, extrinsics re-solved per session |
| Depth | 848 × 480 · 30 fps | Active stereo, 0.3–4 m range, metric millimetres, per-frame confidence map |
| IMU | 200 Hz · 6-DoF | Accel + gyro, bias-calibrated, hardware-timestamped on the same clock domain |
| Hand pose | 21 keypoints × 2 hands | 3D metric, per-joint visibility flag, contact events on grasp and release |
| Body pose | 24 joints | 3D, root-relative and world-frame, torso and forearm chains resolved |
| Segmentation | Instance masks | Manipulated objects + target surfaces, tracked IDs across the episode |
| Action segments | Verb + noun taxonomy | 97 verbs, 512 nouns, start/end to the frame, human-reviewed |
| Formats | RLDS · LeRobot · WebDataset | Also HDF5, zarr, and .rrd for Rerun. Converters shipped as source. |
| License | Commercial · buyer-owned | Perpetual, 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
- Automotive service bay egocentric dataset
- Validated hours
- 640 h — passed sync and QA, shipped to at least one buyer
- Episodes / participants / sites
- 3,980 / 71 / 14
- 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
- Under-vehicle capture is IR-hostile: painted and oily surfaces both absorb the projector pattern. Those episodes carry a depth-quality flag.
- 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 No comparable public set.
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.
No comparable public set
- Hours
- —
- License
- —
There is no public egocentric automotive service corpus of any size. This environment exists because two buyers specified it, and it is the clearest case where a coverage program is the only route to the data.
Firsthand — Automotive service
- Hours
- 640 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 →FAQ
Questions about the automotive service set.
The three we get most often on the first call about this environment.
01How many validated hours of automotive service data are available?
640 validated hours today, across 3,980 episodes from 71 participants at 14 capture sites. A validated hour is footage that passed cross-stream sync and QA and shipped to at least one buyer — raw recording time is higher. Coverage grows as new condition cells are filled.
02What skills does the automotive service dataset cover?
Coverage is specified per skill against a named spec, not per hour. Current skill lines include Torque wrench to spec on a wheel, Fluid drain and fill, Trim and clip removal without damage, and more. Every episode carries frame-accurate action segments so you can filter to the exact skill you are training.
03Can I use the automotive service data to train a commercial model?
Yes. Every set ships under a perpetual, irrevocable, buyer-owned commercial license, with exclusivity available. Each participant signed a written commercial release before capture, faces and license plates are blurred, and un-de-identifiable clips are discarded rather than sold — so the consent chain holds up to legal review.
Pilot automotive service 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.