Frequency-Aware Flow Matching (FAFM) enables continuous and temporally consistent robotic action generation by transforming discrete action sequences into the frequency domain using discrete cosine transform. It regularizes first-order temporal derivatives with a Sobolev-type constraint to ensure smooth actions, improving success rates, motion smoothness, and robustness across synthetic and real-world tasks without adding network parameters.