Google DeepMind’s AI table tennis robot wins 45 percent of matches, excelling against beginners and intermediates.
The robot uses a high-level controller to choose the best skill from a library of low-level skills, each focusing on specific table tennis actions.The system features an industrial robot arm, the ABB IRB 1100, integrated with custom AI software from DeepMind.
In contrast to strictly strategic games like Go or Chess, table tennis necessitates acquiring sophisticated low-level skills and strategic plays. It frequently prefers boldly executable actions over ideal plans. This was achieved through a modular policy design, methods for transferring skills from simulation to reality, real-time adaptation to new opponents, and user studies testing the robot in real-world matches.
“Truly awesome to watch the robot play players of all levels and styles. Going in our aim was to have the robot be at an intermediate level. Amazingly it did just that, all the hard work paid off,” said Barney J. Reed, Professional Table Tennis Coach, who has been working with DeepMind, in aplayers of various skill levels, including beginner, intermediate, advanced, and advanced+, as established by a professional table tennis instructor, in order to assess our agent’s level of proficiency.
According to researchers, the results strongly show that on rallies, the agent performed at an intermediate level of human play.highly for “fun” and “engaging,” regardless of their skill level or match outcome, and most wanted to play again.
Deepmind Google Reinforcemnet Learning Table Tennis
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