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

arXiv: https://arxiv.org/abs/2606.20056 · HTML: https://mlsanity.com/paper/2606.20056/

## Key facts

- No citing papers in the map yet (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 13 of 13)

- [π0.5: a Vision-Language-Action Model with Open-World Generalization](https://mlsanity.com/paper/2504.16054/) (2025)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [DayDreamer: World Models for Physical Robot Learning](https://mlsanity.com/paper/2206.14176/) (2022)
- [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://mlsanity.com/paper/1603.02199/) (2016)
- [HG-DAgger: Interactive Imitation Learning with Human Experts](https://mlsanity.com/paper/1810.02890/) (2018)
- [Correct Me if I am Wrong: Interactive Learning for Robotic Manipulation](https://mlsanity.com/paper/2110.03316/) (2021)
- [Benchmarking the Sim-to-Real Gap in Cloth Manipulation](https://mlsanity.com/paper/2310.09543/) (2023)
- [Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning](https://mlsanity.com/paper/2311.09555/) (2023)
- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changes](https://mlsanity.com/paper/2309.12547/) (2023)
- [A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Control](https://mlsanity.com/paper/2103.08879/) (2021)
- [Variable-Frequency Imitation Learning for Variable-Speed Motion](https://mlsanity.com/paper/2411.12310/) (2024)
- [Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning](https://mlsanity.com/paper/2411.12255/) (2024)

## 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.