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At Eastworlds, we validate our robot data before we sell it. Here, a Unitree G1 autonomously and reliably picks up a bottle using a model trained on Eastworlds' data with just $200 of compute.

Eastworlds

2,508 subscribers

58,543 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

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9 条评论

Eastworlds 的头像

Eastworlds3 个月前

By closing the loop between data collection, model training, and real-world evaluation, we can measure data quality by what ultimately matters: downstream performance.

Eastworlds 的头像

Eastworlds3 个月前

Every trajectory collected through our data platform is independently reviewed through a double-blind QA process. Teleoperators first assess their own demonstrations, before our QA team re-rates them without seeing the original score. This gives us a more reliable measure of data quality and helps ensure that only consistent, high-quality trajectories make it into the final dataset.

Eastworlds 的头像

Eastworlds3 个月前

To make the results reproducible, we’re open-sourcing the dataset used to train this model.

Eastworlds 的头像

Eastworlds3 个月前

Many thanks to the @huggingface and @LeRobotHF teams for building the open-source tooling that made this work possible.

Gekko AI 的头像

Gekko AI3 个月前

🫡

XENON 的头像

XENON3 个月前

Autonomous. Reliable. Trained on real-world data. Humanoid robotics is no longer a prototype , it's a product. S · W · A · Z · R Sensor · Wide · Aerial · Zone · Reconnaissance A name forged from three ancient roots: oath in Proto-Germanic · support in Hebrew · purposeful reach in Arabic USPTO-cleared, globally pronounceable, available now. 🔍 SWAZR on GoDaddy swazr,com

GarethTheDreamer 的头像

GarethTheDreamer3 个月前

$200 compute for that level of autonomy?! That's the kind of efficiency we need 🚀

Wizz🟢 的头像

Wizz🟢3 个月前

$200 bro

Officialsammykeys🦾 的头像

Officialsammykeys🦾3 个月前

❤️

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Today we are introducing Dyna-2, a world-action model pre-trained on one million hours of human video. At this scale, for the first time, we discovered several new scaling laws: • world-action models exhibit scaling law on human data across four orders of magnitude, from 1000 to 1,000,000 hours, • this human data scaling law implied a scaling law on never seen robot data, • both data and objective matter; world modeling and scaling on video data are essential for cross-embodiment scaling transfer to emerge 🧵

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