OMP: One-step Meanflow Policy with Directional Alignment

• URL: http://arxiv.org/abs/2512.19347v2
• Date: Thu, 29 Jan 2026 14:02:10 GMT
• Title: OMP: One-step Meanflow Policy with Directional Alignment
• Abstract summary: One-step MeanFlow Policy (OMP) designed for high-fidelity, real-time manipulation.<n>Experiments on Adroit and Meta-World benchmarks demonstrate that OMP outperforms state-of-the-art methods in success rate and trajectory accuracy.
• Score: 26.114675928221974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robot manipulation has increasingly adopted data-driven generative policy frameworks, yet the field faces a persistent trade-off: diffusion models suffer from high inference latency, while flow-based methods often require complex architectural constraints. Although in image generation domain, the MeanFlow paradigm offers a path to single-step inference, its direct application to robotics is impeded by critical theoretical pathologies, specifically spectral bias and gradient starvation in low-velocity regimes. To overcome these limitations, we propose the One-step MeanFlow Policy (OMP), a novel framework designed for high-fidelity, real-time manipulation. We introduce a lightweight directional alignment mechanism to explicitly synchronize predicted velocities with true mean velocities. Furthermore, we implement a Differential Derivation Equation (DDE) to approximate the Jacobian-Vector Product (JVP) operator, which decouples forward and backward passes to significantly reduce memory complexity. Extensive experiments on the Adroit and Meta-World benchmarks demonstrate that OMP outperforms state-of-the-art methods in success rate and trajectory accuracy, particularly in high-precision tasks, while retaining the efficiency of single-step generation.

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