A Kinesthetic Teaching Framework for Tasks With Contact Transitions and Time-Optimized Execution

In kinesthetic teaching, a robot is manually guided by a human operator to demonstrate a task. Most methods focus on replaying the recorded motion, but are agnostic to contact transitions, which can be critical when interacting with rigid environments. To overcome this limitation, we propose a framework that allows to teach motions in free space as well as in contact while preventing fast unintended contact transitions. This is accomplished by exploiting a projection-based unilateral damping force that increases close to contact. We derive an explicit analytical expression for the damping characteristics to ensure a safe stop before the contact when no further forces act on the robot. Furthermore, after the teaching, the recorded motion data is utilized to generate a time-optimized trajectory based on convex optimization, in which the contact transitions are explicitly considered. We validated our framework in experiments with a torque-controlled manipulator.