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Human hand-object demonstrations can now be retargeted to robots with 90% success and 100x less compute, unlocking vast datasets for dexterous manipulation.
Off-policy RL can now train in minutes instead of hours for sim-to-real robot control, thanks to a new algorithm that borrows scaling laws from supervised learning.
