mlsanityData to 7 Oct 2026

Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth

arXiv 2102.10283 ↗First posted 20 Feb 2021cs.RO cs.SY eess.SYcited by 16

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

Abstract (arXiv)

The generation of robot motions in the real world is difficult by using conventional controllers alone and requires highly intelligent processing. In this regard, learning-based motion generations are currently being investigated. However, the main issue has been improvements of the adaptability to spatially varying environments, but a variation of the operating speed has not been investigated in detail. In contact-rich tasks, it is especially important to be able to adjust the operating speed because a nonlinear relationship occurs between the operating speed and force (e.g., inertial and frictional forces), and it affects the results of the tasks. Therefore, in this study, we propose a method for generating variable operating speeds while adapting to spatial perturbations in the environment. The proposed method can be adapted to nonlinearities by utilizing a small amount of motion data. We experimentally evaluated the proposed method by erasing a line using an eraser fixed to the tip of the robot as an example of a contact-rich task. Furthermore, the proposed method enables a robot to perform a task faster than a human operator and is capable of operating close to the control bandwidth.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 12 of its 49 references that are in the map. First posted on arXiv 20 Feb 2021.

Builds on

12 of 49 references.

Closest ancestors · 3

Same direction, newest first

  1. 2020Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning12 Nov
  2. 2020A Geometric Perspective on Visual Imitation Learning5 Mar
  3. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning25 Oct

Other references · 9

  1. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  2. 2018Residual Reinforcement Learning for Robot Controlcited by 242
  3. 2018Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Controlcited by 21
  4. 2015Highway Long Short-Term Memory RNNs for Distant Speech Recognitioncited by 17
  5. 2019Time Series Motion Generation Considering Long Short-Term Motioncited by 10
  6. 2021Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Taskscited by 10
  7. 2018Deep Learning Scooping Motion using Bilateral Teleoperationscited by 4
  8. 2019Interactive Trajectory Adaptation through Force-guided Bayesian Optimizationcited by 3
  9. 2019Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensorcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 6; 2025: 5; 2026: 3.

16 papers.

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  1. 2021Imitation Learning: Progress, Taxonomies and Challengescited by 56
  2. 2024Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformercited by 27
  3. 2025SAIL: Faster-than-Demonstration Execution of Imitation Learning Policiescited by 16
  4. 2022Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speedcited by 14
  5. 2024ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformercited by 10
  6. 2024DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Imagescited by 7
  7. 2025Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformerscited by 5
  8. 2025Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generationcited by 3
  9. 2024Loss Function Considering Dead Zone for Neural Networkscited by 2
  10. 2024Variable-Frequency Imitation Learning for Variable-Speed Motioncited by 2
  11. 2024Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learningcited by 2
  12. 2025Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulatorscited by 2
4 more
  1. 2026Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulationcited by 2
  2. 2025Improving Low-Cost Teleoperation: Augmenting GELLO with Forcecited by 1
  3. 2026Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learningcited by 1
  4. 2026Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensitycited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021Imitation learning for variable speed motion generation over multiple actions
  2. 2025A Survey on Imitation Learning for Contact-Rich Tasks in Robotics
  3. 2021Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
  4. 2022A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies
  5. 2026Impedance Cloning: Learning Equilibrium Point Parameters for Contact-Rich Manipulation ↗
  6. 2024Proleptic Temporal Ensemble for Improving the Speed of Robot Tasks Generated by Imitation Learning
  7. 2024A Comparison of Imitation Learning Algorithms for Bimanual Manipulation
  8. 2019Contact Skill Imitation Learning for Robot-Independent Assembly Programming