SUREFlow: State-space Uncertainty-aware Residual Flow Matching for Robust Robot Manipulation

Jul 14, 2026

SUREFlow is a new framework for robot manipulation that leverages a Mamba backbone to jointly predict action velocities and input-dependent residual uncertainty, allowing selective refinement of unreliable action dimensions. On the LIBERO benchmark, SUREFlow achieves a 92.5% average success rate, outperforming the Mamba-based MaIL by 34.2%. On LIBERO-PRO, it attains around 49% success rate with only 179M parameters, comparable to much larger vision-language-action models with 3-7B parameters.

Why it matters: SUREFlow shows that explicit uncertainty modeling in action generation can substantially improve the robustness and efficiency of robot manipulation, achieving strong performance with far fewer parameters than existing large models.

Full story at: arXiv Robotics ↗