Can a robot keep up with Serena Williams?
Researchers have taught a humanoid robot to play tennis with humans — and it can hold its own.
Chinese AI robotic company Galbot designed software to teach a Unitree G1 humanoid robot to play tennis against a human engineer.
The company posted a video to social media showing a white robot holding what appears to be an unmodified tennis racket and using it to return the ball as it shuffles across the court.
“Your humanoid tennis player is here!” Galbot wrote on X. “For the first time, a humanoid robot can sustain high-dynamic, long-horizon tennis rallies with millisecond-level reactions, precise ball striking, and natural whole-body motion.”
“This marks a leap from mechanical motion imitation to intelligent, decision-driven athletic interaction.”
The software is dubbed LATENT (Learning Athletic Humanoid Tennis Skills from Imperfect Human Motion Data), and the company claims it’s the world’s first real-time whole-body planning and control algorithm for athletic humanoid tennis.
According to a yet-to-be-peer-reviewed paper, the system had to rely on “imperfect human motion data” consisting only of “motion fragments that capture the primitive skills used when playing tennis” rather than clean motion capture from “real-world tennis matches.”
The short fragments of human movement used were made up of things like forehand swings, backhand strokes and basic footwork. These motion fragments become a library of movement building blocks which the robot stitches together and figures out how to combine them in real time.
When it comes to wrist control, the robot’s high-level controller directly adjusts the wrist during play rather than using the “imperfect” data.
The robot can sustain multi-shot tennis matches with humans, reacting to balls traveling over 15 meters per second, which is about 33.5 miles per hour, and manages to produce coordinated strokes and footwork.
The movements produced look relatively natural — especially for a robot. It’s not exactly fluid like a human, but it’s not rigid and robotic either.
“Our key insight is that, despite being imperfect, such quasi-realistic data still provide priors about human primitive skills in tennis scenarios,” the researchers found.
“With further correction and composition, we learn a humanoid policy that can consistently strike incoming balls under a wide range of conditions and return them to target locations, while preserving natural motion styles.”
In simulation tests, the system achieved up to 96% success in forehand shots.
However, the engineers said that the software could be useful beyond the ability to play tennis.
“Although this work primarily focuses on the tennis return task, the proposed framework has the potential to generalize to a broader range of tasks where complete and high-quality human motion data are unavailable,” they noted.
If a robot can learn a complicated physical skill like tennis from imperfect data, it suggests that similar approaches can work for real-world tasks as well.
Earlier this year, it was reported that bots resembling humans could fold laundry, answer doors and even get you coffee.
