mlsanityData to 14 Oct 2026

Variable-Frequency Imitation Learning for Variable-Speed Motion

arXiv 2411.12310 ↗First posted 19 Nov 2024cs.ROcited by 2

On the map: Offline RL and imitation learning from data → Imitation learning methods

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.

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.

Builds on

13 of 27 references.

References · 13

  1. 2023Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardwarecited by 1,568
  2. 2022DayDreamer: World Models for Physical Robot Learningcited by 400
  3. 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 316
  4. 2024World Models for Autonomous Driving: An Initial Surveycited by 80
  5. 2021Dynamic Movement Primitives in Robotics: A Tutorial Surveycited by 47
  6. 2018Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Controlcited by 21
  7. 2020Aligning Time Series on Incomparable Spacescited by 16
  8. 2021Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidthcited by 16
  9. 2023Soft and Rigid Object Grasping With Cross-Structure Hand Using Bilateral Control-Based Imitation Learningcited by 15
  10. 2022An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applicationscited by 14
  11. 2022Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speedcited by 14
  12. 2023Real-time Motion Generation and Data Augmentation for Grasping Moving Objects with Dynamic Speed and Position Changescited by 6
1 more
  1. 2021A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Controlcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Is Diversity All You Need for Scalable Robotic Manipulation?cited by 20
  2. 2026VFILC: Accurate Frequency Extrapolations in Imitation Learning via Sampling Frequency ILCcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning
  2. 2021Imitation learning for variable speed motion generation over multiple actions
  3. 2025Confounded Causal Imitation Learning with Instrumental Variables
  4. 2022Imitation Learning via Differentiable Physics
  5. 2022Interactive Imitation Learning in Robotics: A Survey
  6. 2023Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation
  7. 2019On Value Discrepancy of Imitation Learning
  8. 2024Fusion Dynamical Systems with Machine Learning in Imitation Learning: A Comprehensive Overview