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Bridging the gap between human and robotic manipulation, HandEdit enables scalable learning for dexterous robotics using abundant egocentric video data.
Time-derived progress labels can mislead robotic learning, but UR-VC corrects these inaccuracies to enhance task success in real-world manipulation.
NativeMEM achieves a staggering success rate of 98.7% on real robots by compressing visual histories into single tokens, revolutionizing long-horizon robotic manipulation.
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.
Forget LLMs' short-sightedness: video generation offers a surprisingly effective path to real-world navigation from high-level goals, achieving 2.5x better success.
