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Open-AoE is an open, community-oriented egocentric manipulation dataset and toolchain that spans from smartphone capture to model training, providing approximately 2,000 hours of manipulation video with annotations and downstream tools for embodied learning.
This paper proposes a bridging action representation based on relative wrist translation in the head-camera frame to transfer human manipulation skills to bi-manual robots, using a vision-language-action model with interleaved action tokens and attention masking to handle embodiment differences.