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AIRoA-0: Learning Long-Horizon, Precise Cross-Embodiment Manipulation from Handheld Data

AIRoA-0 turns 16,151 hours of bimanual handheld-gripper demonstrations into a vision-language-action policy that uses anchored tool motion, selective wrist perception, stage prediction, proprioceptive memory, and streaming decoding to carry one set of weights across four robot embodiments.