Sony AI's 'Ace' is the first machine to reach expert-level play in a competitive physical sport
April 23rd, 2026: Sony AI's Ace defeats elite table tennis playersNew here? Follow stories to track developments over time. Create a free account to get updates when stories you care about change.
A robot beat elite human table tennis players under official competition rules. Sony AI's system, called Ace, returned topspin and backspin shots from professionals during peer-reviewed trials published on Nature's cover April 23—reaching expert-level play for the first time in a commonly played competitive physical sport.
The win is narrower than it sounds—one robot, one sport, one room. But the table tennis problem (perceive a spinning ball, predict trajectory, move the heavy arm to the right place in under half a second) is the same problem industrial robots and warehouse pickers fail at. Ace cleared it using event-based cameras and model-free reinforcement learning, the same tool kit being pushed into factories and logistics.
Why it matters
For the first time, a robot can out-react elite humans in real-world sport, showing that AI can move in the physical world.
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Inside Project Ace: Discover the Robot Athlete That Competes With ...
Inside Project Ace: Discover the Robot Athlete That Competes With ...
Inside Project Ace: Discover the Robot Athlete That Competes With ...
WATCH: A robot is beating human pros at table tennis
Associated Press
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Project Ace
Sony AI
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"A machine that paddles a ball faster than any man alive — and men fret over their dignity. When my locomotives made the horse obsolete, the horse did not hold a senate hearing about it."
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"The machine has mastered the gesture without ever knowing the weight of defeat — and we congratulate ourselves, as though the slave who never tires is a liberation rather than a mirror held up to our own expendability."
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Sony's dedicated artificial intelligence research division, focused on gaming, imaging, gastronomy, and robotics.
One of the most-cited scientific journals in the world; cover papers typically mark results the editors consider field-defining.
May 1997 April 2026
Nature publishes the Ace paper on its cover, describing the first robot to reach expert-level play in a competitive physical sport under official rules.
A learned robot system wins roughly half its matches against intermediate human players, but loses to advanced opponents.
Sony AI's reinforcement-learning agent defeats champion human drivers in a simulated racing environment.
A reinforcement-learning team defeats OG, the reigning Dota 2 world champions, in a real-time strategy video game.
DeepMind's system wins 4-1 against one of the strongest Go players in history, years ahead of expert predictions.
Watson defeats Ken Jennings and Brad Rutter, extending machine performance into open-domain natural language.
IBM's chess engine becomes the first machine to beat a reigning world champion in a match under tournament conditions.
3 moments from history that rhyme with this story — and how they unfolded.
IBM's chess computer Deep Blue beat world champion Garry Kasparov 3.5-2.5 in a six-game match in New York. It was the first time a machine had won a match against a reigning world champion under standard tournament conditions. Kasparov accused IBM of cheating; IBM retired the machine immediately after.
Chess was declared 'solved' in popular coverage, though engines continued to improve for another decade. IBM's stock rose and the PR value was estimated in the hundreds of millions of dollars.
Chess engines now dominate all humans. The result reshaped public expectations for AI and became the template for framing later benchmark wins in Jeopardy, Go, and protein folding.
Deep Blue set the pattern: pick a domain humans consider a signature of intelligence, beat the best human at it, and claim a milestone. Ace follows the same playbook — but in the physical world, where the machine has to move, not just compute.
DeepMind's AlphaGo beat 18-time world champion Lee Sedol 4-1 in a five-game Go match in Seoul, watched by more than 200 million people online. Experts had predicted a win of this kind was at least a decade away. Move 37 in game two, a strategy no human player had considered, became a defining moment for modern AI.
Google DeepMind's profile and AI funding globally surged. China accelerated its national AI strategy, citing the match explicitly.
Reinforcement learning with deep neural networks became the dominant paradigm for game-playing AI and, increasingly, for control problems. AlphaZero and MuZero followed, generalizing the approach.
AlphaGo showed that learned systems could beat experts in domains previously considered beyond AI. Ace uses the same family of methods — model-free reinforcement learning with deep networks — applied to a physical sport rather than a board game.
Google DeepMind published a paper on a robot arm that played competitive table tennis against humans, winning roughly 45% of matches overall. It beat beginners consistently, split matches with intermediate players, and lost to advanced players. The system used a hierarchical policy trained with reinforcement learning and real-world data.
Widely cited as the best robot table tennis system to date and as evidence that sim-to-real transfer was maturing.
Established table tennis as a live benchmark for embodied AI and a direct target for follow-up work.
This is the immediate precursor Ace measures itself against. The jump from 'beats amateurs, loses to pros' to 'beats elite players under competition rules' is the specific gap Sony AI is claiming to close.