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

Author

Listed:
  • Shirine El Zaatari

    (Coventry University)

  • Yuqi Wang

    (Wuhan University of Technology)

  • Yudie Hu

    (Wuhan University of Technology)

  • Weidong Li

    (Coventry University
    Wuhan University of Technology)

Abstract

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.

Suggested Citation

  • Shirine El Zaatari & Yuqi Wang & Yudie Hu & Weidong Li, 2022. "An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1503-1519, June.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:5:d:10.1007_s10845-021-01743-w
    DOI: 10.1007/s10845-021-01743-w
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    References listed on IDEAS

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    1. Peizhi Shi & Qunfen Qi & Yuchu Qin & Paul J. Scott & Xiangqian Jiang, 2020. "A novel learning-based feature recognition method using multiple sectional view representation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1291-1309, June.
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