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 Flow Matching (SL-FM), a source-intervenable flow-matching policy that exposes such a handle while keeping the velocity field shared and latent-free (without a separate discrete-handle input). The handle selects only the source endpoint of the conditional flow, not a mode-specific field, preserving the standard formulation while avoiding decomposition into separate mode-con...
| Authors | He Zhang, Ying Sun, Ziyang Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 July 2026 |
| Last updated at source | 28 September 2026 |
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