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Trajectory creation towards fast skill deployment in plug-and-produce assembly systems: A Gaussian-Mixture Model approach

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conference contribution
posted on 21.03.2019, 13:36 by Melanie Zimmer, Ali Al-YacoubAli Al-Yacoub, Pedro FerreiraPedro Ferreira, Niels LohseNiels Lohse
In this paper, a technique that reduces the changeover time in industrial workstations is presented. A Learning from Demonstration-based algorithm is used to acquire a new skill through a series of real-world human demonstrations in which the human shows the desired task. Initially, the collected data are filtered and aligned applying Fast Dynamic Time Warping (FastDTW). Then the aligned trajectories are modelled with a Gaussian Mixture Model (GMM), which is used as an input to generate a generalisation of the motion through a Gaussian Mixture Regression (GMR). The proposed approach is set into the context of the openMOS framework to efficiently add new skills that can be performed on different workstations. The main benefit of this work in progress is providing an intuitive, simple technique to add new robotics skills to an industrial platform which accelerates the changeover phase in manufacturing scenarios.


The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680735, project openMOS (Open Dynamic Manufacturing Operating System for Smart Plug-and-Produce Automation Components). Funding from the Engineering and Physical Science Research Council Centre for Doctoral Training in Embedded Intelligence (grant no. EP/L014998/1) is also acknowledged.



  • Mechanical, Electrical and Manufacturing Engineering

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2nd UK-RAS ROBOTICS AND AUTONOMOUS SYSTEMS CONFERENCE https://www.ukras.org/wp-content/uploads/2019/03/UKRAS19-Proceedings-Final.pdf




87 - 90 (4)


ZIMMER, M. ... et al, 2019. Trajectory creation towards fast skill deployment in plug-and-produce assembly systems: A Gaussian-Mixture Model approach. Presented at the 2nd UK Robotics and Autonomous Systems Conference (UK-RAS 2019), Loughborough, UK, 24 January 2019, pp.87-90.


EPSRC UK-Robotics and Autonomous Systems (UK-RAS) Network


AM (Accepted Manuscript)

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This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

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This is a conference paper.