Playing table tennis is a difficult motor task that requires fast movements, accurate control, and adaptation to task parameters. Although human beings see and move slower than most robot systems, they significantly outperform all table tennis robots. One important reason for this higher performance is the human movement generation. In this article, we study human movements during table tennis and present a robot system that mimics human striking behavior. Our focus lies on generating hitting motions capable of adapting to variations in environmental conditions, such as changes in ball speed and position. Therefore, we model the human movements involved in hitting a table tennis ball using discrete movement stages and the virtual hitting point hypothesis. The resulting model was evaluated both in a physically realistic simulation and on a real anthropomorphic seven degrees of freedom Barrett WAMTM robot arm.
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