An autonomous desk tennis robotic developed by Sony AI has competed towards and defeated high-level human gamers in regulated matches, in accordance to Reuters. The system is a part of a broader class typically referred to as “physical AI,” the place synthetic intelligence is utilized to machines working in real-world environments.
The robotic, named Ace, was designed to function in a aggressive sport atmosphere that requires fast decision-making and exact motor management. In accordance to the challenge group, it combines high-speed notion techniques with AI-driven management to execute pictures underneath match situations.
Ace competed in matches performed underneath Worldwide Desk Tennis Federation guidelines and officiated by licensed umpires. In trials documented in April 2025, the system gained three out of 5 matches towards elite gamers and misplaced two towards professional-level opponents. Sony AI reported that subsequent matches in December 2025 and early 2026 included wins towards skilled gamers.
Earlier desk tennis robots have existed since the Nineteen Eighties, however they have been not in a position to match the efficiency of superior human gamers. “In contrast to laptop video games, the place prior AI techniques surpass human specialists, bodily and real-time sports activities like desk tennis stay a serious open problem,” stated Peter Dürr, director at Sony AI Zurich and lead of the challenge.
AI techniques have achieved robust ends in digital environments like chess and video video games, the place situations are totally simulated, Dürr stated.
Dürr stated the system was developed to research how robots can reply with velocity and accuracy in dynamic environments. The work was detailed in a research revealed in the journal Nature.
The game presents technical challenges due to the velocity and variability of the ball, together with complicated spin and altering trajectories, which require fast sensing and coordinated motion in tight time constraints, Dürr stated. Ace’s structure consists of 9 synchronised cameras and three imaginative and prescient techniques, which monitor the ball’s motion and spin. The system processes visible knowledge at a velocity ample to seize movement that is troublesome for the human eye to resolve. “This is quick sufficient to seize movement that might be a blur to the human eye,” Dürr stated.
The robotic platform makes use of eight joints to management the racket. Three management positioning, two management orientation, and three handle shot power and velocity. The configuration was designed to meet the minimal mechanical necessities for aggressive play.
In contrast to many AI techniques skilled by way of human demonstration, Ace was skilled in simulation. The strategy allowed it to develop its personal methods, leading to play patterns that differ from human opponents. Dürr stated the system “learns to play not from watching people” however by way of self-training in simulated environments.
Skilled participant Mayuka Taira, who misplaced a match to the system, stated the robotic was troublesome to predict as a result of it reveals no seen cues throughout play. Rui Takenaka, an elite participant who each gained and misplaced towards Ace, stated it dealt with complicated spins properly however was extra predictable on less complicated serves. Taira stated the system’s lack of emotional alerts made it tougher to anticipate its responses. “As a result of you may’t learn its reactions, it’s inconceivable to sense what sort of pictures it dislikes or struggles with,” she stated.
Dürr stated the system demonstrates robust capability in studying ball spin and reacting rapidly, whereas ongoing work focuses on enhancing adaptability throughout matches. The challenge group stated related notion and management strategies could possibly be utilized to areas like manufacturing and repair robotics.
Humanoid robots examined in long-distance race
At the 2026 Beijing E-City Humanoid Robotic Half Marathon, humanoid robots competed over a 21-kilometre course in Beijing. The occasion included greater than 100 robots and roughly 12,000 human individuals, who ran on separate tracks.
A robotic named Lightning, developed by Honor, accomplished the race in 50 minutes and 26 seconds. The time was quicker than Olympic runner Jacob Kiplimo’s 57 minutes and 20 seconds recorded at the Lisbon Half Marathon in March. Lightning collided with a barricade throughout the race however continued and completed first. Honor robots additionally positioned second and third in the competitors. Efficiency improved in contrast to the earlier yr’s occasion, the place the quickest robotic accomplished the course in two hours, 40 minutes and 42 seconds. Organisers stated the occasion was supposed to check humanoid robots in large-scale, real-world situations.
In accordance to Related Press, one other Honor robotic accomplished the course in 48 minutes underneath distant management. Nonetheless, race guidelines prioritised autonomous navigation, and Lightning was recognised as the official winner.
Honor engineers stated applied sciences developed for the robotic, together with structural reliability and liquid-cooling techniques, could possibly be utilized in industrial situations.
(Picture by Mattias Banguese)
See additionally: Cadence expands AI and robotic partnerships with Nvidia, Google Cloud
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