Watch Google DeepMind robot ping pong player take on humans | Digital Trends

Ping pong seems to be the sport of choice when it comes to tech firms showcasing their robotic wares. Japanese firm Omron, for example, made headlines several years ago with its ping pong robot that could comfortably sustain a rally with a human player, showing off the firm’s sensor and control technology in the process.

And with artificial intelligence (AI) recently coming along in leaps and bounds, we’re now starting to see more advanced robotic ping pong players that could soon give even the best human players a run for their money.

Take this impressive effort from engineers at Google DeepMind. In a new paper titled, “Achieving Human Level Competitive Robot Table Tennis,” the team said it’s created a “solidly amateur human-level player” that combines AI with an industrial robotic arm — with a bat attached.

A video (top) shows the AI-powered robot making fast decisions to perform an array of backhand and forehand shots. Notably, it’s also able to return shots that are served with light spin, demonstrating an ability to read the spin on the ball, adjusting the way that it hits the ball accordingly. It’s also able to handle the ball coming at it at high and low speeds, from all parts of the table, as well as balls coming at it from a considerable height after taking a big bounce on the table. It really is very impressive.

“Achieving human-level speed and performance on real world tasks is a north star for the robotics research community,” the researchers said in the paper. “This work takes a step towards that goal and presents the first learned robot agent that reaches amateur human-level performance in competitive table tennis.”

Through a series of test encounters, the robot won 100% of the games that it played against human beginners, and 55% against intermediate players. However, there’s clearly plenty of room for improvement as it lost all of its games against advanced players. Overall, the robot won 45% of the 29 games it played.