An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing

Shirine El Zaatari, Yuqi Wang, Yudie Hu, Weidong Li

    Research output: Contribution to journalArticlepeer-review

    8 Citations (Scopus)
    145 Downloads (Pure)


    Task-Parameterized Learning from Demonstrations (TP-LfD) is an intelligent intuitive approach to support collaborative robots (cobots) for various industrial applications. Using TP-LfD, human’s demonstrated paths can be learnt by a cobot for reproducing new paths for the cobot to move along in dynamic situations intelligently. One of the challenges to applying TP-LfD in industrial scenarios is how to identify and optimize critical task parameters of TP-LfD, i.e., frames in demonstrations. To overcome the challenge and enhance the performance of TP-LfD in complex manufacturing applications, in this paper, an improved TP-LfD approach is presented. In the approach, frames in demonstrations are autonomously chosen from a pool of generic visual features. To strengthen computational convergence, a statistical algorithm and a reinforcement learning algorithm are designed to eliminate redundant frames and irrelevant frames respectively. Meanwhile, a B-Spline cut-in algorithm is integrated in the improved TP-LfD approach to enhance the path reproducing process in dynamic manufacturing situations. Case studies were conducted to validate the improved TP-LfD approach and to showcase the advantage of the approach. Owing to the robust and generic capabilities, the improved TP-LfD approach enables teaching a cobot to behavior in a more intuitive and intelligent means to support dynamic manufacturing applications.
    Original languageEnglish
    Pages (from-to)1503-1519
    Number of pages17
    JournalJournal of Intelligent Manufacturing
    Issue number5
    Early online date6 Feb 2021
    Publication statusPublished - Jun 2022

    Bibliographical note

    Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders. This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.


    This research is funded by the Coventry University, the Unipart Powertrain Application Ltd. (U.K.), the Institute of Digital Engineering, U.K., and a research project sponsored by the National Natural Science Foundation of China (Project No. 51975444)


    • Collaborative robots
    • Learning from demonstration
    • Reinforcement learning

    ASJC Scopus subject areas

    • Software
    • Industrial and Manufacturing Engineering
    • Artificial Intelligence


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