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Warehouse robot training data programs consistently underperform their lab benchmarks in production. The reason is almost never the model architecture. It is almost always a
Surgical robot training data has requirements that no general-purpose robotics data program is built to meet out of the box. Sub-millimeter precision, HIPAA compliance, and
A robotics data quality assurance pipeline is not a checklist or a review meeting. At production scale, robotics data quality requires automated validation, per-operator metrics,
Robot data annotation is not image labeling with a different name. The temporal structure of robot trajectories, the grounding in physical task semantics, and the
Sim-to-real robot training with synthetic data is one of the most powerful techniques in embodied AI — and one of the most misunderstood. The gap
The embodied AI training data problem is structurally different from the language model data problem. Language models learned from the internet. Embodied AI must learn
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