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Autonomous robot competes with elite table tennis players

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Research area:engineering-energy

What the study found

The study found that Ace, an autonomous robot system, was competitive with elite human table tennis players. The authors describe it as, to their knowledge, the first real-world autonomous system to reach that level in table tennis.

Why the authors say this matters

The study suggests that physical AI agents can perform complex, real-time interactive tasks. The authors say this points to broader applications in areas that require fast, precise human-robot interaction.

What the researchers tested

The researchers tested Ace in matches against elite and professional table tennis players under official competition rules. Ace used event-based vision sensors, which detect changes in a scene rather than full images, model-free reinforcement learning, a method that learns control through trial and reward, and high-speed robot hardware.

What worked and what didn't

Ace achieved several victories in matches against elite and professional players. It also showed consistent returns of high-speed, high-spin shots.

What to keep in mind

The abstract does not provide detailed match statistics, the number of games played, or a full description of failure cases. It also limits the claim to the reported table tennis setting and the competition conditions described.

Key points

  • Ace is described as an autonomous robot system competitive with elite human table tennis players.
  • The system combines event-based vision, model-free reinforcement learning, and high-speed robot hardware.
  • It was evaluated in matches against elite and professional players under official competition rules.
  • The abstract says Ace won several matches and returned high-speed, high-spin shots consistently.
  • The authors suggest the work points to broader applications for fast human-robot interaction.

Disclosure

Research title:
Autonomous robot competes with elite table tennis players
Authors:
Peter Dürr, Mireille El Gheche, Guilherme Maeda, Nobuhiko Mukai, Naoya Takahashi, Stefan Heusser, Hamdi Sahloul, Yamen Saraiji, Pavel Adodin, Yin Bi, Sam Blakeman, Christian Conti, Dunai Fuentes Hitos, Yunpu Hu, Farshad Khadivar, Raphaela Kreiser, Luz Martinez, Fabian Schilling, Ricardo Tapiador-Morales, Guillem Torrente, Mario Ynocente Castro, Lison Abecassis, Alberto Giammarino, Yu-Ting Huang, Yannik Nagel, Andrea Scotti, Alexander Sigrist, Tiago Silva, Etienne Walther, Jengyan Wong, Bilan Yang, Asude Aydin, Divij Grover, Apurv Saha, Valentina Cavinato, Takekazu Kakinuma, Taishi Kunori, Valentin Monferrato, Stefan Richter, Stefanos Charalambous, Simon Guist, Mads Alber Kuhlmann-Jorgensen, Lorenzo Miele, Agis Politis, Mattia Scardecchia, Hiroaki Kitano, Peter R. Wurman, Peter Stone, Michael Spranger
Institutions:
HES-SO University of Applied Sciences and Arts Western Switzerland, HES-SO University of Applied Sciences and Arts Western Switzerland, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Computer Science Laboratories, Sony Corporation (United States), Sony Corporation (United States), Sony Corporation (United States), Vienna Biocenter
Publication date:
2026-04-22
DOI:
10.1038/s41586-026-10338-5
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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