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Robots can now learn complex manipulation skills entirely in simulation, thanks to a compositional world model that accurately predicts future states and evaluates progress, leading to a 35-45% performance boost in real-world tasks.
Humanoid robots can now learn complex loco-manipulation skills in diverse real-world environments by watching humans, achieving a 51% performance boost over robot-only training.
Achieve 2.5x higher success in long-horizon robotic manipulation with 90% less data and compute by explicitly aligning training and deployment distributions.
