A multi-modal table tennis robot system
This work addresses the challenge of perception and control in robotic table tennis, but it is incremental as it builds on previous systems with specific improvements.
The authors tackled the problem of robotic table tennis by developing an improved system with high accuracy vision detection and fast robot reaction, resulting in enhanced spin estimation and ball detection using multimodal sensors and novel calibration methods.
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.