让模仿学习模型随运动速度变频，提升变速轨迹的泛化能力。
Variable-Frequency Imitation Learning for Variable-Speed Motion
- 训练时按不同速度调整采样频率，模拟真实运动变化。
- 在插值和外推速度下均提升精度，成功率提高12.5%。
- 适合需要灵活变速控制的机器人任务场景。
传统模仿学习在变速运动中难以外推速度，因其依赖固定采样频率的模型。本文提出变频模仿学习（VFIL），使学习模型在训练时随目标运动速度动态调整采样频率。实验表明，该方法在插值与外推频率标签下的速度精度均有提升，并使整体成功率提高12.5%。
原文摘要 · Abstract (English)
Conventional methods of imitation learning for variable-speed motion have difficulty extrapolating speeds because they rely on learning models running at a constant sampling frequency. This study proposes variable-frequency imitation learning (VFIL), a novel method for imitation learning with learning models trained to run at variable sampling frequencies along with the desired speeds of motion. The experimental results showed that the proposed method improved the velocity-wise accuracy along both the interpolated and extrapolated frequency labels, in addition to a 12.5 % increase in the overall success rate.
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