  In this paper, a method for autonomous segmentation of demonstrated robot
movements is proposed. Position data is clustered into Gaussian mixture models
(GMMs), and an initial set of segments is identified from the Gaussian basis
functions. A Kalman filter is used to detect sudden changes in the contact
force/torque measurements, and this is used to update and verify the initial
segmentation points. The segmentation method is verified experimentally on an
industrial robot.
