GoKawiilFew sports require a more finely honed combination of speed, perception and skill than table tennis. Watching a professional game is a jaw-dropping experience that shows how a player can, through training and physical prowess, estimate ball speed and spin with astounding accuracy, and, by combining this with fast reflexes and agility, achieve the tactical gameplay and incredible pace the sport is known for. Writing in Nature, Dürr et al.1, a new player comes to the fore: an artificial-intelligence agent that combines a robotic arm with an AI-based control system. The system, called Ace, can not only challenge professional players, but also provide valuable insights on human strategy and movement.
Nature 652, 864-865 (2026)
doi: https://doi.org/10.1038/d41586-026-01045-2
References Dürr, P. et al. Nature 652, 886–891 (2026). Wurman, P. R. et al. Nature 602, 223–228 (2022). LeCun, Y. & Bengio, Y. in The Handbook of Brain Theory and Neural Networks (ed. Ar-bib, M. A.) 255–258 (MIT Press, 1998). Haarnoja, T., Zhou, A., Abbeel, P. & Levine, S. in Proc. Int. Conf. Mach. Learn. 80, 1861–1870 (2018). Eschmann, J., Albani, D. & Loianno, G. IEEE Robot. Autom. Lett. 9, 6336–6343 (2024). Campbell, M., Hoane, A. J. Jr & Hsu, F. Artif. Intell. 134, 57–83 (2002). Download references
Competing Interests The authors declare no competing interests.
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