Field Data Collection for Robotics: What Teams Get Wrong
The most common field-capture failure is collecting footage instead of data. A week of warehouse video with no capture spec, no metadata, and no consent trail is a liability on a hard drive — unusable for training and risky to even store.
// key_takeaways
- Footage without spec, metadata, and consent is a liability, not a dataset.
- Design the consent framework before capture, not after.
- QA on-site — re-shoots are cheap in the moment and impossible later.
Mistake two is ignoring consent and rights until after capture. Retroactive consent is somewhere between expensive and impossible. A consent and rights framework — who is filmed, what was agreed, what usage is licensed — has to be designed before anyone presses record.
Mistake three is unstructured capture. Field time is expensive; a capture spec (scenarios, angles, conditions, target counts, naming, metadata schema) multiplies what each site visit yields. The spec should be derived from your edge-case taxonomy, so the field program fills known gaps instead of duplicating the head of your distribution.
Finally: QA in the field, not after. A ten-minute review loop on-site catches unusable framing, broken sensors, and missed scenarios while they can still be re-shot. Discovering them two weeks later costs the whole trip.