Outplaying elite table tennis players with an autonomous robot

, and a new control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware. Evaluated in matches against elite and professional players under official competition rules, Ace achieved several victories and demonstrated consistent returns of high-speed, high-spin shots. These results highlight the potential of physical AI agents to perform complex, real-time interactive tasks, suggesting broader applications in domains requiring fast, precise human-robot interaction.

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Institutions

Publication Details

Journal
Nature
Published
2026-04-22
DOI
https://doi.org/10.1038/s41586-026-10338-5
Citations
1
Primary Topic
Robot Manipulation and Learning
Type
article
Field-Weighted Citation Impact
16.81
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article

Outplaying elite table tennis players with an autonomous robot

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Nature
Robot Manipulation and Learning
16.81
article

Outplaying elite table tennis players with an autonomous robot

Apurv Saha, Yunpu Hu, Peter R. Wurman, Stefanos Charalambous, Mario Ynocente Castro, Guillem Torrente, Dunai Fuentes Hitos, Etienne Walther, Ricardo Tapiador-Morales, Pavel Adodin, Alexander Sigrist, Nobuhiko Mukai, Guilherme Maeda, Fabian Schilling, Yin Bi, Sam Blakeman, Hamdi Sahloul, Asude Aydin, Michael Spranger, Agis Politis, Raphaela Kreiser, Valentina Cavinato, Hiroaki Kitano, Peter Dürr, Mireille El Gheche, Divij Grover, Tiago Silva, Simon Guist, Mads Alber Kuhlmann-Jorgensen, Bilan Yang, Stefan Richter, Lison Abecassis, Taishi Kunori, Lorenzo Miele, Christian Conti, Takekazu Kakinuma, Yamen Saraiji, Farshad Khadivar, Andrea Scotti, Peter Stone, Valentin Monferrato, Stefan Heusser, Mattia Scardecchia, Yannik Nagel, Alberto Giammarino, Luz Martinez, Yu-Ting Huang, Jengyan Wong
article en
1 citations

Abstract

, and a new control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware. Evaluated in matches against elite and professional players under official competition rules, Ace achieved several victories and demonstrated consistent returns of high-speed, high-spin shots. These results highlight the potential of physical AI agents to perform complex, real-time interactive tasks, suggesting broader applications in domains requiring fast, precise human-robot interaction.

NatureVol. 652(8111)
HES-SO University of Applied Sciences and Arts Western Switzerland (CH), Sony Corporation (United States) (US), Sony Computer Science Laboratories (JP), Vienna Biocenter (AT)
Openalex Percentile: Top 1%
Robot Manipulation and Learning
16.81
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