Source-Lifted Flow Matching for Intervenable Multimodal Imitation
Flow-matching policies are promising for imitation learning because they model complex multimodal action distributions. However, their stochasticity is largely passive: repeated sampling may yield diverse behaviors, but users cannot directly choose among valid continuations from the same state. We propose Source-Lifted...
He Zhang, Ying Sun, Pengteng Li et al.
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