Notes from the pipeline.
Technical writing on robot data validation, episode quality, labeling systems, and the compute between capture and training.
The first field notes are in progress. This index will hold measured results, implementation details, and lessons from real data pipelines.
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001
The data bottleneck moved
Why abundant capture makes detection, classification, quantification, and quality control the work that matters.
In progress -
002
Quality gates for robot episodes
What to measure before physical AI data enters behavior cloning, VLA training, reinforcement learning, or evaluation.
In progress -
003
Decode once. Reuse the work.
A systems view of sharing source encodings, frames, timestamps, and features across QA, labels, CV, training, and evals.
In progress
Claims backed by measured runs.
Benchmarks, pipeline diagrams, failure analysis, dataset releases, and the engineering decisions behind RoboRun. No content treadmill—just work worth inspecting.