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

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, can remove that anchor. We present HiFi-UMI, a por

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ethics.ai (27 July 2026), “HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone,” evidence record 14086, https://ethics.ai/record/14086 (originally published by HuggingFace Daily Papers).

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