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

arXiv: https://arxiv.org/abs/2102.10283 · HTML: https://mlsanity.com/paper/2102.10283/

## Key facts

- Cited by 16 papers in the map (data through 14 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.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 12 of 12)

- [Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning](https://mlsanity.com/paper/1910.11956/) (2019)
- [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Control](https://mlsanity.com/paper/1811.03759/) (2018)
- [Highway Long Short-Term Memory RNNs for Distant Speech Recognition](https://mlsanity.com/paper/1510.08983/) (2015)
- [Learning Variable Impedance Control via Inverse Reinforcement Learning for Force-Related Tasks](https://mlsanity.com/paper/2102.06838/) (2021)
- [Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning](https://mlsanity.com/paper/2011.06192/) (2020)
- [Time Series Motion Generation Considering Long Short-Term Motion](https://mlsanity.com/paper/1909.03755/) (2019)
- [A Geometric Perspective on Visual Imitation Learning](https://mlsanity.com/paper/2003.02768/) (2020)
- [Deep Learning Scooping Motion using Bilateral Teleoperations](https://mlsanity.com/paper/1810.10414/) (2018)
- [Interactive Trajectory Adaptation through Force-guided Bayesian Optimization](https://mlsanity.com/paper/1908.07263/) (2019)
- [Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor](https://mlsanity.com/paper/1912.00745/) (2019)

## Built on by (most cited first, 16 of 16)

- [Imitation Learning: Progress, Taxonomies and Challenges](https://mlsanity.com/paper/2106.12177/) (2021)
- [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024)
- [SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies](https://mlsanity.com/paper/2506.11948/) (2025)
- [Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed](https://mlsanity.com/paper/2206.10289/) (2022)
- [ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer](https://mlsanity.com/paper/2401.16653/) (2024)
- [DABI: Evaluation of Data Augmentation Methods Using Downsampling in Bilateral Control-Based Imitation Learning with Images](https://mlsanity.com/paper/2410.04370/) (2024)
- [Bi-LAT: Bilateral Control-Based Imitation Learning via Natural Language and Action Chunking with Transformers](https://mlsanity.com/paper/2504.01301/) (2025)
- [Bi-VLA: Bilateral Control-Based Imitation Learning via Vision-Language Fusion for Action Generation](https://mlsanity.com/paper/2509.18865/) (2025)
- [Loss Function Considering Dead Zone for Neural Networks](https://mlsanity.com/paper/2402.00393/) (2024)
- [Variable-Frequency Imitation Learning for Variable-Speed Motion](https://mlsanity.com/paper/2411.12310/) (2024)
- [Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2412.03252/) (2024)
- [Design and Experimental Validation of Sensorless 4-Channel Bilateral Teleoperation for Low-Cost Manipulators](https://mlsanity.com/paper/2507.06174/) (2025)
- [Bi-HIL: Bilateral Control-Based Multimodal Hierarchical Imitation Learning via Subtask-Level Progress Rate and Keyframe Memory for Long-Horizon Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2603.13315/) (2026)
- [Improving Low-Cost Teleoperation: Augmenting GELLO with Force](https://mlsanity.com/paper/2507.13602/) (2025)
- [Refinement of Accelerated Demonstrations via Incremental Iterative Reference Learning Control for Fast Contact-Rich Imitation Learning](https://mlsanity.com/paper/2604.16850/) (2026)
- [Bi-MoDe: Bilateral Control-based Imitation Learning via Modifier-Conditioned Decoding for Modulation of Execution Speed and Contact Intensity](https://mlsanity.com/paper/2609.16040/) (2026)

## 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.