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Humanoid robots can now learn complex loco-manipulation by transferring visual-motion priors from over 10,000 hours of human data, achieving top performance on simulated and real-world tasks.
PLAT bridges dense motion tracking and sparse goal-conditioned control by exploiting dense goal sequences as privileged supervision during training while requiring only sparse timed keyframe commands at deployment.
