Unsupervised Human Motion Segmentation Based on Characteristic Force Signals of Contact Events

Abstract

Humans perform complex tasks involving force interactions daily. Learning from demonstration, a method for transferring such human manipulation skills to robots, requires techniques for segmenting the demonstrations into movement primitives. Therefore, we propose an unsupervised motion segmentation method that utilizes small characteristic fluctuations of 6-axis force/torque signals as features for motion segmentation. This method includes a feature extraction using a time derivative process and detects segmentation points based on the time derivative of 6-axis force/torque signals obtained during the task demonstrations. The segmentation method was evaluated using a peg-in-hole task and bottle-lid opening task. The experimental results demonstrate the validity of using time derivative of forces and torques for motion segmentation.