Unsupervised stochastic segmentation of behaviour for learning by demonstration
Kwang-Eun Ko, Kwee-Bo Sim · Electronics Letters · 2016
A method for autonomous segmentation of motion primitives from continuous observation of goal‐directed behaviours is proposed. In the proposed method, the iterative patterns of the observed behaviours, which are viewed as motion primitives, are segmented using a stochastic topology preserving map (TPM). The stochastic TPM, which is derived from a combination of self‐organising map and Gaussian mixture, provides substantial capabilities for unsupervised learning and clustering of high‐dimensional data sequences. The results of experiments conducted, in which the proposed method was applied to a dataset of daily activities captured using a Kinect, verify that the proposed method is reliable.