Imitation Learning of Positional and Force Skills Demonstrated via Kinesthetic Teaching and Haptic Input

TLDR

The study proposes a method to learn and reproduce robot force interactions in human–robot interaction settings. The method learns positional and force profiles from kinesthetic teaching and haptic demonstrations, encodes them as a mixture of dynamical systems, and uses task‑space control with variable stiffness to reproduce the skill. The approach was validated in two experiments, where the robot successfully learned and executed an ironing task and a door‑opening task.

Abstract

A method to learn and reproduce robot force interactions in a human–robot interaction setting is proposed. The method allows a robotic manipulator to learn to perform tasks that require exerting forces on external objects by interacting with a human operator in an unstructured environment. This is achieved by learning two aspects of a task: positional and force profiles. The positional profile is obtained from task demonstrations via kinesthetic teaching. The force profile is obtained from additional demonstrations via a haptic device. A human teacher uses the haptic device to input the desired forces that the robot should exert on external objects during the task execution. The two profiles are encoded as a mixture of dynamical systems, which is used to reproduce the task satisfying both the positional and force profiles. An active control strategy based on task-space control with variable stiffness is then proposed to reproduce the skill. The method is demonstrated with two experiments in which the robot learns an ironing task and a door-opening task.