Data collection for physical AI models

RGB video datasets for real‑world manipulation.

Captured in authentic workshops and production environments through trusted partners—designed around the behaviors and edge cases your model needs to learn.

Check samples

How we work with partners on the ground

A short look at real annotation and review workflows, plus what processed output can look like—clear instructions, consistent rubrics, and quality checks so everyone knows what “good” looks like before data ships.

On-site workflow

How capture and review are run day to day with partners.

Annotated data example

Representative view of footage after labeling and QA—formats vary by your spec.

What you can request

Start with a pilot batch, validate model impact, then scale. We’ll help translate your training objective into a dataset spec that’s feasible and high-signal.

Manipulation sequences with hands + objects in natural settings
Single-camera or multi-camera capture setups
Task labels and structured metadata when needed
Annotation, labeling, and QA loops to keep consistency high
Versioned dataset drops in agreed formats

Typical engagement

Pilot

Small batch to validate spec + quality.

Scale

Increase volume with consistent protocols.

Annotation and labeling that matches your training pipeline.

Beyond capture, we help teams turn raw footage into structured training data—using pragmatic specs, review loops, and iteration based on model feedback.

Task labels + metadata

Consistent naming, timestamps, scene context, and manifests.

Human-in-the-loop QA

Spot checks, rubric-driven review, and drift monitoring.

Custom schemas

We’ll align formats to your loaders, evals, and training code.

Iterate with model feedback

Close the loop: update specs based on what improves training.

Sample capture

Representative egocentric RGB clips from domestic and everyday manipulation tasks. Footage is captured in natural environments with hands, objects, and real scene variation.