Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Zeng, Chao, Yang, Chenguang, Cheng, Hong, Li, Yanan and Dai, Shi-Lu (2020) Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning. IEEE Transactions on Industrial Informatics. pp. 1-9. ISSN 1551-3203

[img] PDF - Accepted Version
Download (6MB)


Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach.

Item Type: Article
Keywords: Adaptive Impedance Control, Multimodality, Human-robot interaction systems
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 01 Apr 2020 08:21
Last Modified: 08 Apr 2020 08:00

View download statistics for this item

📧 Request an update