Incremental Kinesthetic Teaching of End-Effector and Null-Space Motion Primitives

In this paper, we propose a unified approach to teach and iteratively refine both end-effector and null-space movements. Hence, the robot can be taught to make use of all its degrees-of-freedom (DoF) to adapt its behavior to new dynamic scenarios. In order to achieve this goal we propose an incremental learning approach in a framework of kinesthetic teaching based on a multi-priority kinematic controller, the so-called Task Transition Control (TTC). The learning algorithm is responsible for skill acquisition and their incremental update. On the real-time level, end-effector and null-space motion primitives, as well as the physical guidance are considered as prioritized tasks. The transitions among these tasks and their insertion and removal are managed by the TTC according to the specified transition parameters. This allows to introduce a customized task which guarantees a proper and smooth response to the applied external forces during the kinesthetic teaching. Experimental results on a 7 DoF KUKA lightweight manipulator show the effectiveness of the proposed approach.