# Frequency-Aware Flow Matching

A team from Lingchu Intelligence with Beihang University, Peking University and the Chinese University of Hong Kong introduces FAFM, a frequency-aware flow-matching method that represents robot actions as frequency coefficients tied to real physical time instead of fixed-length discrete sequences, and explicitly constrains the first time derivative while generating actions by flow matching. Demonstrations recorded at 5 Hz, 10 Hz and 20 Hz can then be mapped onto one physical time scale and trained together without downsampling or interpolation. On a Franka arm, integrating FAFM into π₀.₅ raised success from 80% to 95% on grasp-and-place and from 80% to 90% on multi-obstacle avoidance.

A team from Lingchu Intelligence with Beihang University, Peking University and the Chinese University of Hong Kong introduces FAFM, a frequency-aware flow-matching method that represents robot actions as frequency coefficients tied to real physical time instead of fixed-length discrete sequences, and explicitly constrains the first time derivative while generating actions by flow matching. Demonstrations recorded at 5 Hz, 10 Hz and 20 Hz can then be mapped onto one physical time scale and trained together without downsampling or interpolation. On a Franka arm, integrating FAFM into π₀.₅ raised success from 80% to 95% on grasp-and-place and from 80% to 90% on multi-obstacle avoidance.