Google DeepMind Develops a 'Solidly Amateur' Table Tennis Robot

An anonymous reader quotes a report from TechCrunch: In a newly published paper titled "Achieving Human Level Competitive Robot Table Tennis," Google's DeepMind Robotics team is showcasing its own work on the game. The researchers have effectively developed a "solidly amateur human-level player" when pitted against a human component. During testing, the table tennis bot was able to beat all of the beginner-level players it faced. With intermediate players, the robot won 55% of matches. It's not ready to take on pros, however. The robot lost every time it faced an advanced player. All told, the system won 45% of the 29 games it played. "This is the first robot agent capable of playing a sport with humans at human level and represents a milestone in robot learning and control," the paper claims. "However, it is also only a small step towards a long-standing goal in robotics of achieving human level performance on many useful real world skills. A lot of work remains in order to consistently achieve human-level performance on single tasks, and then beyond, in building generalist robots that are capable of performing many useful tasks, skillfully and safely interacting with humans in the real world." The robot's biggest trouble areas are responding to fast balls, high and low balls. It also has trouble with backhand and the ability to read the spin on an incoming ball. Here's how the researchers plan to address the issue with fast balls: "To address the latency constraints that hinder the robot's reaction time to fast balls, we propose investigating advanced control algorithms and hardware optimizations. These could include exploring predictive models to anticipate ball trajectories or implementing faster communication protocols between the robot's sensors and actuators."

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