Frequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation
Overview
This research introduces Frequency-Aware Flow Matching (FAFM), a method designed to generate continuous and temporally consistent robotic actions. By transforming discrete action sequences into the frequency domain using the discrete cosine transform (DCT) and applying flow matching over coefficients, FAFM addresses brittleness in existing methods that rely on discretized chunks. The approach also regularizes the first-order temporal derivative to suppress high-frequency errors and discourage abrupt changes. Evaluated across synthetic benchmarks (toy, obstacle avoidance, LapGym, LIBERO) and deployed on a real-world Franka robot, FAFM demonstrates improvements in success rates, multimodal expressivity, motion smoothness, convergence speed, and robustness to mechanical bias and mixed-frequency input without introducing additional network parameters.
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“ion models. Across synthetic toy benchmark, obstacle avoidance, LapGym, and LIBERO, FAFM improves success rates, multimodal expressivity, motion smoothness, convergence speed, robustness to mechanical bias and mixed-frequency input. These gains are consistent when deployed on a real-world Franka robot. Code available at https://github.com/gakakulicc/FAFM.”
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- arXiv Robotics — research abstractsSignalFrequency-Aware Flow Matching for Continuous and Consistent Robotic Action Generation
This research introduces Frequency-Aware Flow Matching (FAFM), a method designed to generate continuous and temporally consistent robotic actions. By transforming discrete action sequences into the frequency domain using the discrete cosine transform (DCT) and applying flow matching over coefficients, FAFM addresses brittleness in existing methods that rely on discretized chunks. The approach also regularizes the first-order temporal derivative to suppress high-frequency errors and discourage abrupt changes. Evaluated across synthetic benchmarks (toy, obstacle avoidance, LapGym, LIBERO) and deployed on a real-world Franka robot, FAFM demonstrates improvements in success rates, multimodal expressivity, motion smoothness, convergence speed, and robustness to mechanical bias and mixed-frequency input without introducing additional network parameters.
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