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.
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Acknowledgements
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). We would also acknowledge reviewers for their valuable and constructive comments for us to improve the manuscript.
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The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
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Zaatari, S.E., Wang, Y., Hu, Y. et al. An improved approach of task-parameterized learning from demonstrations for cobots in dynamic manufacturing. J Intell Manuf 33, 1503–1519 (2022). https://doi.org/10.1007/s10845-021-01743-w
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DOI: https://doi.org/10.1007/s10845-021-01743-w