Free-View Manipulation via Dual-Flow Generation Policy: Canonical Images and Actions

Visuomotor policies have become the mainstream action generation head in robot manipulation research. However, in open-world environments, the camera viewpoint varies arbitrarily and breaks the perception–action spatial alignment, leading to poor generalization and unstable execution. To address this issue, we propose a novel Dual-Flow Generation Policy (DFGP) that implicitly learns viewpoint-invariant visual representations to adapt to free-view observations and guide accurate manipulation action generation. Our framework consists of two collaborative pathways: an image mapping pathway that infers canonical-view images from free-view inputs via flow matching, and an action generation pathway that predicts robot actions conditioned on the shared canonical visual features. We further introduce a Bi‑directional Cross‑Attention (BiXT) module to adaptively fuse multi-source features for stronger cross-view action generalization. Extensive experiments conducted on two simulation benchmarks (Free-View and LIBERO) —as well as on physical world robotic platforms demonstrate that DFGP achieves statistically significant improvements over prior visuomotor policies.