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Ace, the table tennis robot, not only matches but beats professional players, showcasing a groundbreaking leap in robotic agility and precision.
Achieving real-time spin estimation with just 3 ms latency could revolutionize how we analyze and enhance performance in professional sports.
Real-time human-robot table tennis rallies are now possible, thanks to a novel event-camera dataset and advanced ball-state estimation techniques.
A transformer-based predictor narrows the sim-to-real gap in robotic table tennis by leveraging extensive real-world data and attention mechanisms for long-range forecasting.
