# SignIQ Research Preview v0

**Build date:** 2026-04-25

This bundle contains every interactive sample currently published on
the SignIQ Sample Lab — across embodiments and modalities — so robotics
ML teams can poke at SignIQ data without uploading anything or
authenticating to any service.

Each sample is a self-contained directory with:

- `manifest.json` — top-level descriptor (id, robot, control freq, cameras, modalities, format exports)
- `metadata.json` — schema (state/action shape + names), task instruction, subtask spans, attribution
- `timeseries.json` — dense state, action, gripper, wrench / IMU, phase-per-frame
- `trajectory.json` — 3D-projected end-effector or joint paths
- `health.json` — 10–12 automated QA checks per episode
- `videos/*.mp4` — H.264 / yuv420p, browser-playable, frame-synchronized to the per-frame data

The data is real episode capture (state, action, timestamps, video).
Sample data origin and underlying license are recorded in each
`metadata.json`'s `attribution` block. Production deliveries are SignIQ
commercial captures recorded in our facilities under explicit consent.

## What's inside

See `DATA_CARD.md` for the per-sample inventory: embodiment, frame count,
control frequency, state and action dimensions, camera count, license tag.

## Loading

`load_lerobot.py` is a minimal Python loader that reads any sample and
prints its summary. The bundle layout follows the same conventions as
LeRobot v2.1 (per-bundle metadata + per-frame parquet-style time
series), so existing LeRobot tooling will work after a thin adapter.

```bash
python3 load_lerobot.py samples/signiq-aloha-coffee
```

## Trying it interactively

The same bundles power https://signiq-lab-data.pages.dev/samples — each
sample's inspector renders synchronized cameras, action vs. state
charts, joint-space trajectories, and the QA health panel.

## Talking to us

For a custom collection run, GR00T-LeRobot bundles, or anything not in
this preview, reach the SignIQ data team via the dataset request form
on the website.
