How humanoid robots learn to perceive and act, and why the human-labeled data underneath decides which ones actually work.

Robotics annotation runs on judgment calls, not just labels. Here's why who does the labeling matters as much as how it's done.
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A humanoid that spars with no one at the controls, a $100 million warehouse robot raise, Samsung reorganizing to build its own humanoid, and a fresh bet on the layer beneath every robot.
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A warehouse robot and a home robot aren't learning from the same data. How training data requirements shift by deployment environment, and what stays constant no matter where a robot works.
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Nscale bets $3.5B of compute on Figure, a dual-arm robot ties rebar on a building site, Medtronic puts $700M into surgical robotics, and Nvidia pushes real inference to the edge.
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Robot recordings capture behavior that unfolds over time. Annotation is what turns that into actions, states, events and outcomes a learning system can use.
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Skild's S1 learns from one video, Figure scales human data with Index, humanoids race in Beijing, and XPENG raises to build IRON.
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Simulation can accelerate robot training, but the physical world doesn't follow the rules of a simulator. Here's where simulated data works, where the reality gap appears, and why real-world data still matters.
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Diversity, sensor coverage, and consistent annotation matter more than raw volume. Here's how to tell if a dataset can actually teach a robot.
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From observation to operation: how robots acquire real-world skills.
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Robots don't have to learn every job from scratch. They start with us.
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