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Field Vision & Operational Robotics Dataset.

Measurement today. The training data robotics will need tomorrow.

the problem

What this is actually for

The race for physical-world training data is already on — the big labs are paying for it. What most operators do not realize is that they are already generating it: skilled physical work, in real cluttered rooms, photographed every day. It just walks out the door unstructured and unowned.

proof

What we built for ourselves

Vision models score our after-job photography for material composition, diversion rate and completion quality. That part is live and pays for itself as measurement.

The photo archive is searchable by description instead of filename, using image and text embeddings.

In development: first-person capture, scene reconstruction, and simulation environments for training manipulation policies against real clutter rather than synthetic scenes.

the deliverable

What you get

  • Vision grading of field photography — composition, diversion, quality
  • Semantic search across your photo archive
  • A structured operational record you own, accumulating from work you already do and already photograph

Hear how it got built

We recorded the build of each of these, including the parts that didn't work.

  • Episode 1 — The Robotics Thesisrecording
  • Episode 11 — Who Gets To Distribute The Robotsrecording

Episodes publish as they're cut. Nothing here is a teaser for something that doesn't exist — the systems are already running.

the honest limit

What this doesn't do

A robot that clears a garage is not a near-term product, and anyone telling you otherwise is selling something. This is marked Developing on purpose. What makes the position credible is that the capture is funded by measurement that already pays for itself — so the dataset accrues whether or not the long thesis lands on schedule.

under the hood

What it runs on

Built on NVIDIA hardware and software we own and operate, not a reseller relationship.

DGX SparkJetsonMetropolisCUDAOmniverse & Isaac (in development)Pythonembedding models

Where it's going next: Capture pipeline running daily. The gate is shift coverage, not technology.

NVIDIA Inception Program member badge

A member of the NVIDIA Inception program

Fifteen minutes, no deck.

Bring one specific problem in one sentence. We'll tell you whether it's worth building, roughly what it takes, and sometimes that the answer is don't build it.

Start here

Two steps before we talk — it takes about five minutes.

also