A table tennis-playing robot can keep up a rally against humans, but like many amateur players, it struggles when attempting fancier shots. Researchers at the University of Tübingen in Germany began by designing a computer simulation in which a virtual robot arm equipped with a table tennis racket attempted to return ping pong balls across a virtual table tennis table. Researchers ran this simulation so that a machine learning algorithm could learn how the velocity and orientation of the racket affects the path the ball takes. Once this algorithm, which learns by trial and error, could reliably return the ball, the researchers set it up to control the movement of a real robot arm positioned next to a real table.
The system used two cameras to track the location of the real ball every 7 milliseconds, and the algorithm processed the signals and decided where to move the robotic arm to hit and return the ball. The signals that the algorithm sent allowed the robot arm to accurately play shots to within an average of 24.9 centimeters of the intended location. This accuracy level was slightly worse than when the algorithm was working with a simulation – a common occurrence, as computer simulations can’t accurately represent everything in real life. The entire process – including training in the virtual simulation and in the real world took just 1.5 hours, demonstrating how rapidly algorithms can learn to perform in a new situation.
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