How to Brief a Data Collection Vendor: Writing an Effective Task Spec
The success of any AI data collection campaign is determined during the task specification phase. A precise, unambiguous brief ensures the vendor configures the right sensor hardware, trains operators accurately, and delivers datasets ready for immediate model training.
Essential Elements of an AI Task Specification
- Granular task taxonomy: Defining discrete sub-goal phases (e.g. reach, grasp, lift, insert, release) rather than broad task labels.
- Sensor modalities & camera angles: Specifying field-of-view, frame rate, resolution, and exact camera mounting coordinates.
- Failure & edge-case criteria: Explicitly defining what constitutes a failed trial and whether failed demonstrations should be logged as negative training samples.
- Target schema & data loader: Specifying exact HDF5 datasets, RLDS structures, or OpenUSD asset formats.
Frequently Asked Questions
Should a task brief be tested with a pilot batch first?
Always — running a 50-episode pilot reveals edge cases in the spec that can be refined before scaling to 10,000+ episodes.
Where Blue Projects Fits In
Blue Projects collaborates with client engineering teams to refine task specifications and offers free matched sample batches to validate requirements before full commitment.
See the Data Before You Commit
Evaluate our physical AI capture quality firsthand. Request a free matched sample batch delivered in your target schema (HDF5, RLDS, WebDataset) or browse our active Google Drive repository.