Mean Flow Matching for PushT Task: One-Step Action Generation

Created in April 01, 2025

2025   ·   robotics   diffusion   flow-matching   imitation-learning   ·   research

This post explores applying Mean Flow Matching to the PushT task — a planar pushing benchmark commonly used to evaluate robot learning policies.

Standard diffusion-based policies require many denoising steps at inference time, making them slow for real-time control. Mean Flow Matching reframes the generation process by learning the mean velocity field of the probability flow ODE, which enables collapsing the entire trajectory into a single-step inference without sacrificing quality.

Read the full write-up on Medium: Mean Flow Matching for PushT Task: One-Step Action Generation



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