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.
Computational models of motor control and learning that describe human motion generation can be useful for neuroscientists to verify hypotheses of human motor control. These models can also be useful in robotics to create robot systems that are able to perform a wide variety of movements robustly and adapt these movements to unexpected environmental conditions and new requirements. In contrast to previous robot table tennis approaches, we use an anthropomorphic robot arm with seven degrees of freedom and concentrate on generating smooth movements that properly distribute the forces over the different degrees of freedom. To cope with the resulting challenges of this approach, we use a biomimetic approach for trajectory generation and movement adaptation based on theories pertaining to human motor control in table tennis.