Sony AI robot beats players as humanoid robot wins Beijing race

An autonomous table tennis robot developed by Sony AI has competed against and defeated high-level human players in regulated matches, according to Reuters. The system is part of a broader category often referred to as “physical AI,” where artificial intelligence is applied to machines operating in real-world environments.

The robot, named Ace, was designed to operate in a competitive sport environment that requires rapid decision-making and precise motor control. According to the project team, it combines high-speed perception systems with AI-driven control to execute shots under match conditions.

Ace competed in matches conducted under International Table Tennis Federation rules and officiated by licensed umpires. In trials documented in April 2025, the system won three out of five matches against elite players and lost two against professional-level opponents. Sony AI reported that subsequent matches in December 2025 and early 2026 included wins against professional players.

Previous table tennis robots have existed since the 1980s, but they were not able to match the performance of advanced human players. “Unlike computer games, where prior AI systems surpass human experts, physical and real-time sports like table tennis remain a major open challenge,” said Peter Dürr, director at Sony AI Zurich and lead of the project.

AI systems have achieved strong results in digital environments like chess and video games, where conditions are fully simulated, Dürr said.

Dürr said the system was developed to study how robots can respond with speed and accuracy in dynamic environments. The work was detailed in a study published in the journal Nature.

The sport presents technical challenges due to the speed and variability of the ball, including complex spin and changing trajectories, which require rapid sensing and coordinated movement in tight time constraints, Dürr said. Ace’s architecture includes nine synchronised cameras and three vision systems, which track the ball’s movement and spin. The system processes visual data at a speed sufficient to capture motion that is difficult for the human eye to resolve. “This is fast enough to capture motion that would be a blur to the human eye,” Dürr said.

The robotic platform uses eight joints to control the racket. Three control positioning, two control orientation, and three manage shot force and speed. The configuration was designed to meet the minimum mechanical requirements for competitive play.

Unlike many AI systems trained through human demonstration, Ace was trained in simulation. The approach allowed it to develop its own strategies, resulting in play patterns that differ from human opponents. Dürr said the system “learns to play not from watching humans” but through self-training in simulated environments.

Professional player Mayuka Taira, who lost a match to the system, said the robot was difficult to predict because it shows no visible cues during play. Rui Takenaka, an elite player who both won and lost against Ace, said it handled complex spins well but was more predictable on simpler serves. Taira said the system’s lack of emotional signals made it harder to anticipate its responses. “Because you can’t read its reactions, it’s impossible to sense what kind of shots it dislikes or struggles with,” she said.

Dürr said the system demonstrates strong ability in reading ball spin and reacting quickly, while ongoing work focuses on improving adaptability during matches. The project team said similar perception and control techniques could be applied to areas like manufacturing and service robotics.

Humanoid robots tested in long-distance race

At the 2026 Beijing E-Town Humanoid Robot Half Marathon, humanoid robots competed over a 21-kilometre course in Beijing. The event included more than 100 robots and approximately 12,000 human participants, who ran on separate tracks.

A robot named Lightning, developed by Honor, completed the race in 50 minutes and 26 seconds. The time was faster than Olympic runner Jacob Kiplimo’s 57 minutes and 20 seconds recorded at the Lisbon Half Marathon in March. Lightning collided with a barricade during the race but continued and finished first. Honor robots also placed second and third in the competition. Performance improved compared to the previous year’s event, where the fastest robot completed the course in two hours, 40 minutes and 42 seconds. Organisers said the event was intended to test humanoid robots in large-scale, real-world conditions.

According to Associated Press, another Honor robot completed the course in 48 minutes under remote control. However, race rules prioritised autonomous navigation, and Lightning was recognised as the official winner.

Honor engineers said technologies developed for the robot, including structural reliability and liquid-cooling systems, could be applied in industrial scenarios.

(Photo by Mattias Banguese)

See also: Cadence expands AI and robotic partnerships with Nvidia, Google Cloud

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