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We present an implementation of an online optimization algorithm for hittinga predefined target when returning ping-pong balls with a table tennis robot.The online algorithm optimizes over so-called interception policies, whichdefine the manner in which the robot arm intercepts the ball. In our case,these are composed of the state of the robot arm (position and velocity) atinterception time. Gradient information is provided to the optimizationalgorithm via the mapping from the interception policy to the landing point ofthe ball on the table, which is approximated with a black-box and a grey-boxapproach. Our algorithm is applied to a robotic arm with four degrees offreedom that is driven by pneumatic artificial muscles. As a result, the robotarm is able to return the ball onto any predefined target on the table afterabout 2-5 iterations. We highlight the robustness of our approach by showingrapid convergence with both the black-box and the grey-box gradients. Inaddition, the small number of iterations required to reach close proximity tothe target also underlines the sample efficiency. A demonstration video can befound here: https://youtu.be/VC3KJoCss0k.

Data-Efficient Online Learning of Ball Placement in Robot Table Tennis