If you've ever watched professional ping-pong players on TV, you may have wondered if -- due to their rapid-fire pace of play -- they were actually robots.
Well wonder, no more. A German research team created the first robot that can not only play ping-pong like a human, but can also improve its game by learning from its human opponents like the ones you've maybe seen whizzing around the table in a blur.
The robot -- the brainchild of PhD candidate and robotic researcher Katharina Muelling at the Technical University of Darmstadt -- consists of a robotic arm to which a ping-pong paddle is attached, as well as a camera that provides a view of the table and area of play.
Researcher Katharina Muelling poses with a ping pong playing robot she and her team at the Technical University of Darmstadt in Germany designed and built. The robot is comprised of an arm to which a paddle is attached as well as a camera that watches the table and area of play, responding to the opponent's moves. (Source: The Technical University of Darmstadt)
Muelling's work focuses on developing robots that can achieve motor control and perform complex motor-oriented tasks, as well as learn to adapt as they perform them -- a concept called "kinesthetic teach-in." She outlined her work on the robot in a paper available online.
As described by Muelling, she and her team used table tennis as a "benchmark task" to design the mixture of motor primitives (MoMP) algorithm controlling the robot, allowing it to understand one basic set of movements and then dynamically apply those movements as it goes along. (Watch a video of this process below.)
"The goal of this task is to learn autonomously from and with a human to return a table tennis ball to the opponent's court and to adapt its movements accordingly," she wrote in the paper, titled "Learning to Select and Generalize Striking Movements in Robot Table Tennis."
Once the robot learns movements from a human teacher -- the kinesthetic teach-in aspect of the basic task -- the machine's MoMP is programmed according to these movements. Following this, the robot's learning system identifies the movements it takes to hit the ball, generalizes them, and is able to apply them later throughout a game or prolonged play with an opponent, Muelling wrote. "The resulting system is able to return balls served by a ball launcher as well as to play in a match against a human."
From the looks of the video, Muelling's robotic ping-pong player isn't quite ready for the Olympics yet, but it could provide a steady opponent for practice -- the equivalent of a mechanical ball pitcher for batting practice or playing tennis against a wall.
For robotics researchers, her work lends itself to the creation of future machines that can intuitively learn tasks and adapt their movements accordingly, work other researchers also are bolstering.
This is astounding. It's not hard to imagine this robot beating all humans in ten years. By the way, I notice no one hits to the robot's "backhand." I wonder why.
I must say, 88% return rate is much better than me. But I would be surprised if it could beat a series of new players consistently. Artificial intelligence may seem like a fantasy far into the future, but simple forms of artificial learning are already possible, and are quite formidable. This is definitely the first step in the right direction.
From the bot's perspective, the ball is probably moving in slow motion. A 60Hz sample rate is near in-human. Average reaction time in humans hovers around 200ms.
The first sentence is pretty funny. I've often wondered if some people were robots: not only in sports, but in customer service conversations, both on the phone and by email. As robots get more humanoid looking that's going to be harder to determine even with visual cues.
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