In recent years, robotic table tennis has become a popular research challenge
for perception and robot control. Here, we present an improved table tennis
robot system with high accuracy vision detection and fast robot reaction. Based
on previous work, our system contains a KUKA robot arm with 6 DOF, with four
frame-based cameras and two additional event-based cameras. We developed a
novel calibration approach to calibrate this multimodal perception system. For
table tennis, spin estimation is crucial. Therefore, we introduced a novel, and
more accurate spin estimation approach. Finally, we show how combining the
output of an event-based camera and a Spiking Neural Network (SNN) can be used
for accurate ball detection.Comment: Accepted for RoboLetics: Workshop on Robot Learning in Athletics
@CoRL 202