Chinese researchers say a humanoid robot has taken a surprising step toward learning athletic skills.
The robotic tennis player recently pulled off a shocking rally against a human opponent after teaching itself to play in a way similar to how human athletes train: through drills and repetition. Researchers say the achievement could mark a milestone that opens the door for robots to master complex physical skills far beyond sports.
The work comes from a at Beijing’s Tsinghua University and Peking University, working with the fast-rising robotics startup Galbot. Their training framework, called LATENT (short for Learning Athletic Humanoid Tennis Skills from Imperfect Motion Data) teaches robots athletic movements using incomplete examples of human motion.
Instead of recording full tennis matches, the team fed the robot’s artificial intelligence incomplete snippets of motion data captured from motion-tracking recordings of amateur tennis players. The snippets included basic tennis techniques such as forehand swings, backhands and footwork patterns. Though imperfect, the researchers said the data still contained valuable information about how athletes move.
Using machine learning, the system corrected and recombined these fragments into complete tennis actions the robot could learn. A digital twin of the humanoid then practiced thousands of variations in simulation, where conditions constantly changed to prepare it for the chaotic realities of live tennis. By experimenting with countless scenarios, the virtual robot learned which movements worked best.
For the real-world tests, the team customized a Unitree G1 humanoid robot. They attached a tennis racket to the robot’s right arm using a 3D-printed adapter, replacing the robot’s hand so it could strike the ball. Reflective markers were also added so cameras in an optical motion-capture system could track the robot’s position and movement during rallies.
In real-world experiments, the robot returned incoming tennis balls with a success rate of more than 90 percent for forehand shots and about 78 percent for backhands. While its skills still pale in comparison to a trained human player, the researchers say future systems could through robot vs. robot training.