Improved Learning Accuracy for Learning Stable Control from Human Demonstrations

Abstract

Learning from Demonstration (LfD) has been identified as an effective method for making robots adapt to a similar kind of tasks. In this work, a framework of learning from demonstration has been proposed for modelling robot motions. We present an approach based on dimension ascending to learn a dynamical system, so that the reproduced motions can closely follow the demonstrations. In addition, the reproductions can ultimately reach and stop at the target, which reflects the robustness of the method. Therefore, the system accuracy and stability can be better guaranteed simultaneously. The effectiveness of the proposed approach is verified by performing handwriting experiments on the LASA data set.