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A robotization pipeline is developed that converts heterogeneous human videos into robot-aligned observations and action trajectories while inferring missing intermediate signals across annotation levels and constructs the HuRo dataset, comprising about 630K robotized episodes and 142M processed frames from five human-video sources.
RLDX-1 achieves double the success rate of existing VLAs on complex humanoid tasks, suggesting a leap in robots' ability to handle contact-rich, dynamic manipulation.
