A New Phase Determination Algorithm for Iterative Learning of Human-Robot Collaboration

In this paper we discuss a methodology for learning human-robot collaboration tasks by human guidance. In the proposed framework, the robot learns the task in multiple repetitions of the task by comparing and adapting the performed trajectories so that the robot's performance naturally evolves into a collaborative behavior. When comparing the trajectories of two learning cycles, the problem of accurate phase determination arises because the imprecise phase determination affects the precision of the learned collaborative behavior. To solve this issue, we propose a new projection algorithm for measuring the similarity of two trajectories. The proposed algorithm was experimentally verified and compared to the performance of dynamic time warping in learning of human-robot collaboration tasks with Franka Emika Panda collaborative robot.