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Learning compliant robotic movements based on biomimetic motor adaptation

Zeng, Chao; Chen, Xiongjun; Wang, Ning; Yang, Chenguang


Chao Zeng

Xiongjun Chen


It is one of the great challenges for a robot to learn compliant movements in interaction tasks. The robot can easily acquire motion skills from a human tutor by kinematics demonstration, however, this becomes much more difficult when it comes to the compliant skills. This paper aims to provide a possible solution to address this problem by proposing a two-stage approach. In the first stage, the human tutor demonstrates the robot how to perform a task, during which only motion trajectories are recorded without the involvement of force sensing. A dynamical movement primitives (DMPs) model which can generate human-like motion is then used to encode the kinematics data. In the second stage, a biomimetic controller, which is inspired by the neuroscience findings in human motor learning, is employed to obtain the desired robotic compliant behaviors by online adapting the impedance profiles and the feedforward torques simultaneously. Several tests are conducted to validate the effectiveness of the proposed approach.


Zeng, C., Chen, X., Wang, N., & Yang, C. (2021). Learning compliant robotic movements based on biomimetic motor adaptation. Robotics and Autonomous Systems, 135,

Journal Article Type Article
Acceptance Date Oct 13, 2020
Online Publication Date Oct 21, 2020
Publication Date Jan 1, 2021
Deposit Date Nov 10, 2020
Publicly Available Date Oct 22, 2021
Journal Robotics and Autonomous Systems
Print ISSN 0921-8890
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 135
Article Number 103668
Keywords Compliant Robotic Movements; Biomimetic Motor Control; Impedance Adaptation; Learning from Demonstration (LfD); Human-Robot Interaction and Collaboration
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