Learning optimal variable admittance control for rotational motion in human-robot co-manipulation

In this paper the problem of variable admittance control in human-robot cooperation tasks is investigated, considering rotational motion of the robot’s end-effector. A Fuzzy Model Reference Learning algorithm is used to determine online the appropriate virtual damping of the admittance controller with partial state representation of the system. The learning algorithm is trained according to the minimum jerk trajectory model for rotational motion by exploiting the measured angular velocity and the torque applied by the operator. Experiments conducted for a rotational movement of an LWR robot in cooperation with multiple subjects, indicate that the method is able to react to the movement characteristics, by improving low effort cooperation and accurate positioning.