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Can handheld human demonstrations teach precise, long-horizon robot manipulation without collecting new robot trajectories? This episode explains AIRoA-0's learning pipeline, experimental evidence, and public release, separating its operational achievement from the stronger claim of human-only robot learning. We cover three questions: how YUBI's shared gripper interface supports transfer between people and robot arms; how visual gating, stage prediction, memory, and streaming action generation work together; and how to interpret the real-robot task evaluation. The system targets bimanual manipulation, with quantitative evaluation on Franka and transfer demonstrations on TX-G2, OpenArm, and UR platforms. The central limitation is inherited robot pre-training: A-0 starts from π0.5, whose public base checkpoint already learned from robot data. Avoiding additional robot collection is therefore distinct from eliminating robot experience throughout training. We also clarify what is available as of November 6, 2026: post-training code and model weights are released, while the full demonstration dataset is being prepared for publication. What changes when the scalable unit of robot teaching becomes a tool in a person's hand?
