Efficient Learning from Demonstration for Manufacturing Tasks

In response to the evolving landscape of mass customization manufacturing, this work introduces a novel optimization-based smoothing algorithm to enhance task demonstrations. Traditional programming methods in the manufacturing industry face challenges in adapting to frequent reconfiguration demands, leading to increased downtime and costs. Learning from Demonstration (LfD) emerges as a solution, but existing methods often struggle with the quality of human demonstrations, impacting critical metrics such as time, accuracy, and energy efficiency. The proposed algorithm addresses these limitations through a hierarchical optimization scheme, an intuitive velocity feedback procedure, and the incorporation of a smooth function, enabling a trade-off between velocity and accuracy. This approach offers an efficient solution for refining task demonstrations and optimizing key metrics in the context of mass customization manufacturing.