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

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 seg- mentation 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.