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The paper introduces Kinematic MeanFlow (K-MF), a one‑step action generation policy for Robotic Foundation Models that addresses instability in the MeanFlow framework. By decoupling the time derivative into two sub‑interval terms, K-MF captures early and late denoising dynamics separately, reducing error amplification. Experiments show K-MF achieves faster inference—reducing action‑head latency by 67.5%–74.4% and overall end‑to‑end latency by 30.3%–54.9%—while outperforming multi‑step flow matching on various tasks.
HybridFlow is a generative policy for robotic manipulation that uses a three‑stage inference procedure requiring only two network function evaluations (2‑NFE). The policy first generates a coarse action trajectory with a Global Jump based on MeanFlow, then refines the state using a parameter‑free ReNoise interpolation, and finally performs a Local Refine to query the instantaneous‑velocity limit. Experiments on RoboMimic and five real‑robot settings show that HybridFlow achieves high success rates and improves task performance over a 16‑step Diffusion Policy while reducing action‑generation latency by roughly eightfold.
arXiv:2609.36471v1 Announce Type: cross Abstract: World-Action Models (WAMs) improve robotic manipulation by conditioning action generation on predicted future observations, but future prediction add...
arXiv:2607. 29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations--not recursive rollout.
arXiv:2606. 05254v1 Announce Type: new Abstract: World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control.
CF‑VLA introduces a two‑stage coarse‑to‑fine approach for vision‑language‑action policies, replacing multi‑step sampling with a coarse initialization that constructs an action‑aware starting point and a single‑step refinement that corrects residual errors. The coarse stage learns a conditional posterior over endpoint velocity to transform Gaussian noise into a structured initialization, while the fine stage performs a fixed‑time refinement. Experiments on CALVIN and LIBERO demonstrate that CF‑VLA achieves a strong efficiency‑performance trade‑off, reducing action sampling latency by 75.4 % and achieving an 83.0 % real‑robot success rate, outperforming existing NFE=2 methods and matching or surpassing NFE=10 baselines.