Robotic learning from demonstration enabled by improved gaussian mixture model and gaussian mixture regression

Robotic learning from demonstration (LfD) can significantly minimize robotic programming difficulties due to its intuitiveness and efficiency. Gaussian mixture model (GMM)/Gaussian mixture regression (GMR) is an effective method to implement LfD. However, there are limitations in GMM/GMR, such as the requirement of creating multiple demonstrations, sensitivity to demonstration defects, and underfitting/overfitting generalization in industrial tasks. To tackle the issues, in this study, an improved GMM/GMR approach with three improvements is proposed. Firstly, a Gaussian noise scattering strategy is designed to minimize the required number of multiple demonstrations and eliminate unsmooth turning areas/jitters defects in the demonstration. Secondly, based on a new evaluation criterion of incorporating the Gaussian cluster number, Bayesian information criterion, and dynamic time warping, the particle swarm optimization algorithm is introduced to achieve an optimal balance between feature preservation and smoothness for a regression solution, thereby effectively mitigating the overfitting/underfitting issues. Thirdly, the B-spline curve-based reconstruction of the regression solution is devised to enhance the adaptability of GMM/GMR to the changes in target positions. Experiments on retired product recycling are conducted to validate the effectiveness of the approach presented in this study, providing a reliable solution to facilitate LfD in industrial environments.