Chao Zeng
Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration
Zeng, Chao; Yang, Chenguang; Zhong, Junpei; Zhang, Jianwei
Abstract
© 2013 IEEE. Learning a task such as pushing something, where the constraints of both position and force have to be satisfied, is usually difficult for a collaborative robot. In this work, we propose a multimodal teaching-by-demonstration system which can enable the robot to perform this kind of tasks. The basic idea is to transfer the adaptation of multi-modal information from a human tutor to the robot by taking account of multiple sensor signals (i.e., motion trajectories, stiffness, and force profiles). The human tutor's stiffness is estimated based on the limb surface electromyography (EMG) signals obtained from the demonstration phase. The force profiles in Cartesian space are collected from a force/torque sensor mounted between the robot endpoint and the tool. Subsequently, the hidden semi-Markov model (HSMM) is used to encode the multiple signals in a unified manner. The correlations between position and the other three control variables (i.e., velocity, stiffness and force) are encoded with separate HSMM models. Based on the estimated parameters of the HSMM model, the Gaussian mixture regression (GMR) is then utilized to generate the expected control variables. The learned variables are further mapped into an impedance controller in the joint space through inverse kinematics for the reproduction of the task. Comparative tests have been conducted to verify the effectiveness of our approach on a Baxter robot.
Citation
Zeng, C., Yang, C., Zhong, J., & Zhang, J. (2019). Encoding Multiple Sensor Data for Robotic Learning Skills from Multimodal Demonstration. IEEE Access, 7, 145604-145613. https://doi.org/10.1109/ACCESS.2019.2945484
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 28, 2019 |
Online Publication Date | Oct 3, 2019 |
Publication Date | Jan 1, 2019 |
Deposit Date | Oct 29, 2019 |
Publicly Available Date | Oct 30, 2019 |
Journal | IEEE Access |
Electronic ISSN | 2169-3536 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 7 |
Pages | 145604-145613 |
DOI | https://doi.org/10.1109/ACCESS.2019.2945484 |
Keywords | General Engineering; General Materials Science; General Computer Science |
Public URL | https://uwe-repository.worktribe.com/output/4191813 |
Files
Encoding Multiple Sensor Data for Robotic Learning Skills From Multimodal Demonstration
(32.4 Mb)
PDF
Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
You might also like
Impedance learning for human-guided robots in contact with unknown environments
(2023)
Journal Article
A novel human-robot skill transfer method for contact-rich manipulation task
(2023)
Journal Article
A human-robot collaboration method for uncertain surface scanning
(2023)
Journal Article
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search