Learning-based control strategy for safe human-robot interaction exploiting task and robot redundancies

We propose a control strategy for a robotic manipulator operating in an unstructured environment while interacting with a human operator. The proposed system takes into account the important characteristics of the task and the redundancy of the robot to determine a controller that is safe for the user. The constraints of the task are first extracted using several examples of the skill demonstrated to the robot through kinesthetic teaching. An active control strategy based on task-space control with variable stiffness is proposed, and combined with a safety strategy for tasks requiring humans to move in the vicinity of robots. A risk indicator for human-robot collision is defined, which modulates a repulsive force distorting the spatial and temporal characteristics of the movement according to the task constraints. We illustrate the approach with two human-robot interaction experiments, where the user teaches the robot first how to move a tray, and then shows it how to iron a napkin.