VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILC

2026-06-18

AI Summary

This work addresses the instability and frequency inaccuracies inherent in existing neural network-based imitation learning when extrapolating to higher movement speeds. While variable-frequency imitation learning (VFIL) suffers from significant frequency errors under high-speed extrapolation due to its open-loop architecture, this study introduces iterative learning control (ILC) into the VFIL framework for the first time, establishing a closed-loop system that combines feedforward and feedback mechanisms. The feedforward component enables frequency-aware speed extrapolation, while the feedback module online corrects frequency deviations. Evaluated across three distinct tasks, the proposed method substantially enhances both frequency accuracy and motion stability: when extrapolating to twice the average training speed, frequency errors are reduced by 81% and 50% in wiping and shaking tasks, respectively; even in interpolation scenarios involving complex friction in a mixed task, accuracy improves by 27%.

Abstract

Conventional neural network (NN)-based imitation learning methods for variable-speed motion either restricted their scope to interpolated speeds, or generated unpredictable motions when extrapolating beyond trained velocity ranges. Variable-frequency imitation learning (VFIL) enabled extrapolations of speeds by linking the NN model's sampling frequency to the motion frequency, whereas its open-loop configuration caused frequency errors, especially in the extrapolated high-frequency settings. This study proposes variable-frequency imitation learning with iterative learning control (VFILC) based on a combination of VFIL and iterative learning control (ILC) with both feedforward and feedback parts, the former taking advantage of VFIL and the latter adjusting the frequency errors. The experimental results showed that the proposed method successfully and accurately extrapolated motion speeds and reduced frequency errors in all three tasks, and that the feedback especially reduced the frequency errors by a remarkable 81% in the wiping task and 50% in the shaking task, both compared to simple feedforward VFIL, when extrapolating at double the average speed in the training data. The proposed method also improved accuracy by 27% compared with VFIL even at an interpolated frequency for a contact-rich mixing task affected by complex friction traits.

Problem

• imitation learning
• frequency extrapolation
• variable-speed motion
• frequency error
• neural network

Innovation

• Variable-frequency imitation learning
• Iterative learning control
• Frequency extrapolation
• Feedback correction
• Motion imitation