Summary
The report evaluates HiFi-UMI across three policy backbones and four bimanual tabletop tasks, finding success differences of -2.5, +3.1 and -0.6 percentage points against in-domain real-robot teleoperation baselines. It also reports 85% success on a precision-insertion task and lower offline action-prediction error with pretraining, while limiting the conclusions to the tested models, tasks and conditions.
Why it matters
High-fidelity open physical data could reduce reliance on expensive teleoperation, although the reported parity remains narrowly scoped.