Motion Generation Using Bilateral Control-Based Imitation Learning With Autoregressive Learning

Sasagawa, Ayumu; Sakaino, Sho; Tsuji, Toshiaki

doi:10.1109/access.2021.3054960

Cited by 21 publications

(19 citation statements)

References 36 publications

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“…Therefore, it is possible to realize object operation at a rate comparable to that of humans, and high adaptability to environmental changes is achieved. A detailed explanation can be found in [26] [27].…”

Section: ) Maintaining Control Gainsmentioning

confidence: 99%

“…1. The model of the robots was assumed to be the same as that in [27]. However, the physical parameters of the robot were different and were identified on the basis of [33].…”

Section: A Setupmentioning

confidence: 99%

“…This controller was implemented to imitate human object manipulation skills. A four-channel bilateral controller was implemented similar to that in [27].…”

Section: B Controllermentioning

confidence: 99%

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

Sakaino

Fujimoto²,

Saigusa³

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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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.

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Section: ) Maintaining Control Gainsmentioning

confidence: 99%

“…1. The model of the robots was assumed to be the same as that in [27]. However, the physical parameters of the robot were different and were identified on the basis of [33].…”

Section: A Setupmentioning

confidence: 99%

See 1 more Smart Citation

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

Sakaino

Fujimoto²,

Saigusa³

et al. 2022

IEEE Open J. Ind. Electron. Soc.

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“…Contrarily, commands for a follower are responses of a leader during bilateral control. Therefore, both commands and responses for a follower are available in bilateral control-based imitation learning [31]. Therefore, our bilateral control-based imitation learning enables robots to operate quickly owing to using the predicted leader state as a command value.…”

Section: Bilateral Control-based Imitation Learningmentioning

confidence: 99%

“…Subsequently, we proposed bilateral control-based imitation learning that enables robots to execute tasks requiring force adjustment and fast behavior [28]- [32]. Bilateral control is a remote-control technique for leader and follower robots with force feedback [31]. The force information collected with bilateral control is helpful for learning the movement primitive.…”

Section: Introductionmentioning

confidence: 99%

An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications

2022

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Recently, motion generation by machine learning has been actively researched to automate various tasks. Imitation learning is one such method that learns motions from data collected in advance. However, executing long-term tasks remains challenging. Therefore, a novel framework for imitation learning is proposed to solve this problem. The proposed framework comprises upper and lower layers, where the upper layer model, whose timescale is long, and lower layer model, whose timescale is short, can be independently trained. In this model, the upper layer learns long-term task planning, and the lower layer learns motion primitives. The proposed method was experimentally compared to hierarchical RNN-based methods to validate its effectiveness. Consequently, the proposed method showed a success rate equal to or greater than that of conventional methods. In addition, the proposed method required less than 1/20 of the training time compared to conventional methods. Moreover, it succeeded in executing unlearned tasks by reusing the trained lower layer.INDEX TERMS Bilateral control, imitation learning, motion planning, robot learning.

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Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

2022

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Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers motion speed to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful actions among the data obtained during autonomous operations. By fine-tuning the successful data using speed labels, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded more than 75% for other objects.

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Motion Generation Using Bilateral Control-Based Imitation Learning With Autoregressive Learning

Cited by 21 publications

References 36 publications

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

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

An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

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