Table Tennis

Table tennis research currently focuses on developing robotic systems capable of playing the game at a human-competitive level, achieving this through advancements in computer vision, robotic control, and machine learning. Key research areas include accurate ball trajectory prediction (often incorporating physical models and neural networks), real-time spin estimation using diverse sensor modalities (including event cameras), and robust stroke detection and classification from video data. These advancements have implications for both robotics (e.g., improving dexterity and real-time decision-making in dynamic environments) and sports analytics (e.g., enabling objective performance evaluation and training tools).