Imitation learning for variable speed motion generation over multiple actions
On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning
Abstract (arXiv)
Robotic motion generation methods using machine learning have been studied in recent years. Bilateral control-based imitation learning can imitate human motions using force information. By means of this method, variable speed motion generation that considers physical phenomena such as the inertial force and friction can be achieved. However, the previous study only focused on a simple reciprocating motion. To learn the complex relationship between the force and speed more accurately, it is necessary to learn multiple actions using many joints. In this paper, we propose a variable speed motion generation method for multiple motions. We considered four types of neural network models for the motion generation and determined the best model for multiple motions at variable speeds. Subsequently, we used the best model to evaluate the reproducibility of the task completion time for the input completion time command. The results revealed that the proposed method could change the task completion time according to the specified completion time command in multiple motions.
Key facts. Cited by 3 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 34 references that are in the map. First posted on arXiv 11 Mar 2021.
Builds on
12 of 34 references.
Closest ancestors · 1
Same direction, newest first
- 2020Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning12 Nov
Other references · 11
- 2020A Survey on Contrastive Self-supervised Learningcited by 554
- 2018Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Reviewcited by 482
- 2016Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collectioncited by 316
- 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 289
- 2017DART: Noise Injection for Robust Imitation Learningcited by 191
- 2019Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrationscited by 107
- 2020Sim2Real Transfer for Reinforcement Learning without Dynamics Randomizationcited by 47
- 2018Imitation Learning for Object Manipulation Based on Position/Force Information Using Bilateral Controlcited by 21
- 2017Baxter's Homunculus: Virtual Reality Spaces for Teleoperation in Manufacturingcited by 19
- 2017Probabilistic Learning of Torque Controllers from Kinematic and Force Constraintscited by 7
- 2020Adversarial Generation of Informative Trajectories for Dynamics System Identificationcited by 3
Built on by
Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2025SAIL: Faster-than-Demonstration Execution of Imitation Learning Policiescited by 18
- 2022An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applicationscited by 14
- 2022Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speedcited by 14
- 2025SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies13 Jun 2025
- 2022Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed21 Jun 2022
- 2022An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications16 Mar 2022
Similar, not cited
Close in wording, no citation either way
- 2021Imitation Learning for Variable Speed Contact Motion for Operation up to Control Bandwidth
- 2019Time Series Motion Generation Considering Long Short-Term Motion
- 2021Bilateral Control-Based Imitation Learning for Velocity-Controlled Robot
- 2024Variable-Speed Teaching-Playback as Real-World Data Augmentation for Imitation Learning
- 2024Proleptic Temporal Ensemble for Improving the Speed of Robot Tasks Generated by Imitation Learning
- 2026Force Generative Imitation Learning: Bridging Position Trajectory and Force Commands through Control Technique
- 2026Hierarchical Proportion Models for Motion Generation via Integration of Motion Primitives
- 2021Robotic Imitation of Human Assembly Skills Using Hybrid Trajectory and Force Learning