- DeepMind’s robot table tennis player combines an ABB IRB 1100 robotic arm with custom AI software, achieving amateur-level competition against humans.
- The robot won 45% of matches in trials with 29 participants, demonstrating a 100% win rate against beginners and 55% against intermediate players.
- The system utilizes high-speed cameras and a motion-capture setup to adapt its strategies in real-time based on the opponent’s playing style.
- Training involved a hybrid approach of reinforcement learning in a simulated environment, using real-world data to refine the robot’s skills over seven cycles.
- Players reported an enjoyable experience competing against the robot, highlighting the potential for AI in sports training and entertainment, despite some limitations in handling fast or spin-heavy balls.
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Published on: August 25, 2023
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