Learning dynamic graffiti strokes with a compliant robot

We present an approach to generate rapid and fluid drawing movements on a compliant Baxter robot, by taking advantage of the kinematic redundancy and torque control capabilities of the robot. We concentrate on the task of reproducing graffiti-stylised letter-forms with a marker. For this purpose, we exploit a compact lognormal-stroke based representation of movement to generate natural drawing trajectories. An Expectation-Maximisation (EM) algorithm is used to iteratively improve tracking performance with low gain feedback control. The resulting system captures the aesthetic and dynamic features of the style under investigation and permits its reproduction with a compliant controller that is safe for users surrounding the robot.