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About this paper
Jialin Tian, Zhuofan Li, Xinkai Wang, Bokai Lin, Chenyi Wang, Xinyu Zhan, Cewu Lu, Lixin Yang
UniFlowWAM trains robot policies to predict actions, visual features, and three-dimensional scene flow: scene-point positions and their frame-to-frame displacements. Flow shapes training but is not required at deployment; the method uses monocular RGB demonstrations without depth sensing. Across four real-world bimanual tasks, it averages 87.0% full-task success versus 68.5% for π₀.₅, though this comparison does not isolate flow’s effect.
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