Journal article Open Access

Analysis of Methods for Incremental Policy Refinement by Kinesthetic Guidance

Simonič, Mihael; Petrič, Tadej; Ude, Aleš; Nemec, Bojan

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.

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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