Learning Center
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The data problem hiding inside embodied AI
Embodied AI runs on data that has to be produced under a protocol rather than collected from the web, which turns model quality into an operations problem.
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What separates a manipulation dataset that works from one that doesn't
Two manipulation datasets of the same size can differ completely in what they teach a policy. Five design decisions, made before collection, account for most of the difference.
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Beyond sim-to-real: the data gaps simulation can't close
Simulation solves cost and volume for robot training data, but four classes of signal stay out of reach at any fidelity, and each one has to be captured in the real world.
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How to collect preference data for creative and design models
Learn how to collect high-quality preference data for creative AI models using multi-attribute rubrics, structured annotation interfaces, and disagreement routing.
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What is dexterous manipulation data?
Learn what dexterous manipulation data is, why it differs from standard sensor streams, and how annotation structure determines whether a policy learns anything useful.
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Why dexterous manipulation is the hardest data in robotics
Dexterous manipulation projects don't fail at the hardware layer. They fail at annotation. Here's why labeling tactile and force data is uniquely hard.
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How many human demonstrations does it take to train a robot?
The answer isn't a fixed number. It depends on how demonstrations are labeled. Learn what determines whether your dataset produces a deployable robot policy.
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Why taste is a data problem: labeling for subjective quality
Subjective quality labeling fails when teams treat annotator disagreement as noise. Learn why disagreement is the signal, and how to measure it.
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What is designer-annotated preference data?
Designer-annotated preference data captures multi-dimensional design judgment (typography, hierarchy, color) that standard AI training data can't. Here's how it works.
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When generalist annotators aren't enough
Generalist annotators don't fail because they lack expertise. They fail without the right workflow. Learn the tiered model that changes that.
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What is expert annotation?
Expert annotation uses credentialed specialists, not crowd workers, to label AI training data. Learn how it works, why generalist labeling fails, and how to run the program.
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Human data vs. synthetic data: What's the difference?
Learn the real difference between human and synthetic data, where each breaks down, and how the 10 percent threshold rule determines whether your model holds up in production.
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How to choose a human data provider
Stop choosing human data providers by workforce size. Use this rubric covering domain fit, quality control, and workflow integration to find the right provider.
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What does "human data" actually mean?
"Human data" means three different things across three industries. Here's what it means in AI development, and why the supply is shrinking.
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Where robot training data comes from in 2026
Robot training data can't be scraped. Learn where it actually comes from, what makes an episode worth keeping, and how teams decide what to label.
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How to audit the language annotations in your VLA dataset
Learn how to audit VLA dataset language annotations with four structured checks: instruction diversity, grounding, temporal alignment, and density.
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What data do you need to train a VLA model?
Training a VLA model isn't a volume problem. Learn why data composition, diversity, and annotation quality matter more than how many episodes you collect.
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Should you run RLHF in-house or bring in a partner?
The cost-vs-control framing leads RLHF teams astray. Learn the three criteria that actually predict whether in-house or partner annotation will work.
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How to shortlist annotation vendors for your use case
A technical framework for shortlisting annotation vendors: task mapping, four production-fit criteria, pilot design, and data portability before you sign.
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How to run a pilot before committing to an annotation vendor
Learn a five-phase annotation vendor pilot framework that tests process, scalability, and contracts, not just label quality, before you sign.
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Should you build a data collection team or outsource it?
Build vs. outsource your data collection team? Use these three criteria (data complexity, time-to-scale, and domain expertise) to make the right call.
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How vendors keep text, image, audio, and video in sync
Learn why multimodal sync is a data and architecture problem, not a playback one, and how aligned training triplets determine model quality.
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Crowdsourced vs. managed labeling: which fits your project?
Discover the three factors that actually determine whether crowdsourced or managed labeling fits your project, beyond the standard scale vs. quality framing.
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How data labeling pricing models compare
Unit costs don't tell the full story. Learn how automation maturity, domain expertise, and ownership model determine your real data labeling spend.