Classification of Discrete and Rhythmic Movement for Humanoid Trajectory Planning

Recent approaches towards trajectory planning for humanoid robots have divided trajectories into two categories: point-to-point and rhythmic. The former has been implemented traditionally using splines while the latter recently has been implented using mechanisms like pattern generators and neural oscillators. Researchers typically use knowledge of the task to be executed or empirical comparison to determine whether a point-to-point planner or a rhythmic planner is to be used. However, task knowledge is not always available a priori and empirical comparison is time consuming. We present a classifier that automatically determines whether a task is point-to-point or rhythmic using a sample of motion from that task. We evaluate this method on manually labeled motion-capture data of human tasks as well as on Dynamic Motor Primitives, a powerful mechanism for generating point-to-point and rhythmic trajectories. Additionally, we show that our classifier is capable of classifying movements that humans are unable to label as either discrete or rhythmic.