Abstract
Traditional robot programming is often not feasible in small-batch production, as it is time-consuming, inefficient, and expensive. To shorten the time necessary to deploy robot tasks, we need appropriate tools to enable efficient reuse of existing robot control policies. Incremental Learning from Demonstration (iLfD) and reversible Dynamic Movement Primitives (DMP) provide a framework for efficient policy demonstration and adaptation. In this paper, we extend our previously proposed framework with improvements that provide better performance and lower the algorithm’s computational burden. Further, we analyse the learning stability and evaluate the proposed framework with a comprehensive user study. The proposed methods have been evaluated on two popular collaborative robots, Franka Emika Panda and Universal Robot UR10.
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Materials Availability
The data that support the findings of this study are available from the corresponding author, B.N., upon reasonable request.
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Funding
The research leading to these results has received funding from the Horizon 2020 RIA Programme grant 820767 CoLLaboratE and from the program group P2-0076 Automation, robotics, and biocybernetics funded by the Slovenian Research Agency.
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Conceptualization, B.N. and M.S.; methodology, B.N., M.S., A.U. and T.P.; software, M.S. and B.N.; formal analysis, B.N., M.S., A.U. and T.P.; investigation, M.S.; data curation, M.S. and T.P.; writing–original draft preparation, B.N.; writing–review and editing, M.S., B.N. and A.U.; visualization, M.S. and B.N.; supervision, B.N. and A.U.; funding acquisition, B.N. and A.U. All authors read and approved the final manuscript.
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Simonič, M., Petrič, T., Ude, A. et al. Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance. J Intell Robot Syst 102, 5 (2021). https://doi.org/10.1007/s10846-021-01328-y
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DOI: https://doi.org/10.1007/s10846-021-01328-y