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arXiv · 2603.09415

From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation

Ju Dong·Liding Zhang·Lei Zhang·Yu Fu·Kaixin Bai·Zoltan-Csaba Marton·Zhenshan Bing·Zhaopeng Chen·Alois Christian Knoll·Jianwei Zhang
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Abstract

Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on iterative Ordinary Differential Equation (ODE) integration introduces substantial latency, limiting high-frequency closed-loop control. Recent single-step acceleration methods alleviate this overhead but often exhibit distributional collapse, producing averaged trajectories that fail to execute coherent manipulation strategies. We propose a framework that distills a Conditional Flow Matching (CFM) expert into a fast single-step student via Implicit Maximum Likelihood Estimation (IMLE). A bi-directional Chamfer distance provides a set-level objective that promotes both mode coverage and fidelity, enabling preservation of the teacher multi-modal action distribution in a single forward pass. A unified perception encoder further integrates multi-view RGB, depth, point clouds, and proprioception into a geometry-aware representation. The resulting high-frequency control supports real-time receding-horizon re-planning and improved robustness under dynamic disturbances.

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Ju Dong, Liding Zhang, Lei Zhang, Yu Fu, Kaixin Bai, Zoltan-Csaba Marton, Zhenshan Bing, Zhaopeng Chen, Alois Christian Knoll, Jianwei Zhang. 2026-03-10. From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation. https://arxiv.org/abs/2603.09415

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