Kinesthetic teaching of bi-manual tasks with known relative constraints

Kinesthetic teaching allows the direct skill transfer from the human to the robot and has been widely used to teach single arm tasks intuitively. In the bi-manual case, simultaneously moving both end-effectors is challenging due to the high physical and cognitive load imposed to the user. Thus, previous works on bi-manual task teaching resort to less intuitive methods by teaching each arm separately. This in turn requires motion synthesis and synchronization before execution. In this work, we leverage knowledge from the relative task space to facilitate a kinesthetic demonstration by guiding both end-effectors which is more human-like and intuitive way for performing bi-manual tasks. Our method utilizes the notion of virtual fixtures and inertia minimization in the null space of the task. The controller is experimentally validated in a bi-manual task which involves the drawing of a preset line on a workpiece utilizing two KUKA IIWA7 R800 robots. Results from ten participants were compared with a gravity compensation scheme demonstrating improved performance.