HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

arXiv:2607.25895v1 Announce Type: new Abstract: Learning deployable manipulation policies is bottlenecked by the scarcity of data that is both high-fidelity and scalable. Real-robot teleoperation is accurate but costly to scale; robot-free UMI capture scales readily, and current practice uses the resulting data mainly for pre-training, adding a small real-robot "anchor" at post-training. We ask whether raising the fidelity of robot-free UMI data, rather than shrinking the real-robot fraction, ca...