An approach for robotic leaning inspired by biomimetic adaptive control

Zeng, Chao, Su, Hang, Li, Yanan, Guo, Jing and Yang, Chenguang (2022) An approach for robotic leaning inspired by biomimetic adaptive control. IEEE Transactions on Industrial Informatics, 18 (3). pp. 1479-1488. ISSN 1551-3203

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How to enable robotic compliant manipulation has become a critical problem in the robotics field. Inspired by a biomimetic adaptive control strategy, this work presents a novel representation model named human-like compliant movement primitives (Hl-CMPs) which could allow a robot to learn human-like compliant behaviours. The state-of-the-art approaches can hardly learn complete compliant profiles for a specific task. Comparatively, our model can encode task-specific parametric movement trajectories, correspondingly associated with dynamic trajectories including both impedance and feedforward force profiles. The compliant profiles are learned based on a biomimetic control strategy derived from the human motor learning in the muscle space, enabling the robot to simultaneously learn the impedance and the force while executing the movement trajectories obtained from human demonstration. Furthermore, both the kinematic and the dynamic profiles are learned in the parametric space, thus enabling the representation of a skill using corresponding parameters (i.e, task-specific parameters).

Item Type: Article
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Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 04 Jun 2021 07:39
Last Modified: 11 Feb 2022 16:00

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