Meet ‘Ace,’ the paddle-wielding robot who just beat humans at ping pong in AI breakthrough

A paddle-wielding robot is so adept at playing table tennis that it is posing a tough challenge to elite human players and sometimes defeating them, according to a new study that shows how advances in artificial intelligence are making robots more agile. Japanese electronics giant Sony built the robotic arm it calls Ace and pitted it against professional athletes. Ace proved a worthy adversary, though one with some non-human attributes: nine camera eyes positioned around the court and an uncanny ability to follow the ball’s logo to measure its spin. The robot learned how to play the sport using the AI method known as reinforcement learning. “There’s no way to program a robot by hand to play table tennis. You have to learn how to play from experience,” said Sony AI researcher Peter Dürr, co-author of the study published Wednesday in the science journal Nature. To conduct the experiments, Sony built an Olympic-sized table tennis court at its headquarters in Tokyo to give professional and other highly skilled athletes a “level playing field” with the robot, Dürr said in an interview with The Associated Press. Some of the athletes said they were surprised by Ace’s prowess. Sony calls it a first for a common competitive sport Sony says it is the “first time a robot has achieved human, expert-level play in a commonly played competitive sport in the physical world — a longstanding milestone for AI and robotics research.” The custom-built robot has eight joints that direct its movements, or degrees of freedom, enabling it to position the racket, execute shots and swiftly respond to its opponent’s rallies. “Speed is really one of the fundamental issues in robotics today, especially in scenarios or environments that are not fixed,” said Michael Spranger, president of Sony AI, in an interview. “We see a lot of robots that are in factories that are very, very fast,” Spranger said. “But they’re doing the same trajectory over and over again. With this technology, we show that it…