# Variable-Frequency Imitation Learning for Variable-Speed Motion

arXiv: https://arxiv.org/abs/2411.12310 · HTML: https://mlsanity.com/paper/2411.12310/

## Key facts

- Cited by 2 papers in the map (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 27 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## 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)

- [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)
- [World Models for Autonomous Driving: An Initial Survey](https://mlsanity.com/paper/2403.02622/) (2024)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Aligning Time Series on Incomparable Spaces](https://mlsanity.com/paper/2006.12648/) (2020)
- [Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth](https://mlsanity.com/paper/2102.10283/) (2021)
- [Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learning](https://mlsanity.com/paper/2311.09555/) (2023)
- [An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications](https://mlsanity.com/paper/2203.08619/) (2022)
- [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)

## Built on by (most cited first, 2 of 2)

- [Is Diversity All You Need for Scalable Robotic Manipulation?](https://mlsanity.com/paper/2507.06219/) (2025)
- [VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILC](https://mlsanity.com/paper/2606.20056/) (2026)

## Abstract (arXiv)

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.