VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILC
On the map: Offline RL and imitation learning from data → Imitation learning methods
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 13 of its 26 references that are in the map. First posted on arXiv 18 Jun 2026.
Builds on
13 of 26 references.
Closest ancestors · 2
Same direction, newest first
- 2024Variable-Frequency Imitation Learning for Variable-Speed Motion19 Nov
- 2018HG-DAgger: Interactive Imitation Learning with Human Experts5 Oct
Other references · 11
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- 2021Correct Me if I am Wrong: Interactive Learning for Robotic Manipulationcited by 24
- 2023Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learningcited by 15
- 2022Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speedcited by 14
- 2023Benchmarking the Sim-to-Real Gap in Cloth Manipulationcited by 14
- 2023Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changescited by 6
- 2021A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Controlcited by 5
- 2024Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learningcited by 1
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Close in wording, no citation either way
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- 2023Exploring the use of deep learning in task-flexible ILC ↗
- 2022Learning and Extrapolation of Robotic Skills using Task-Parameterized Equation Learner Networks
- 2021Imitation learning for variable speed motion generation over multiple actions
- 2022Imitation Learning via Differentiable Physics
- 2022Interactive Imitation Learning in Robotics: A Survey
- 2022An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications
- 2025IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning