If you thought robots were only built for carrying boxes, vacuuming floors, or fetching you a drink, think again. A team of researchers has managed to teach a four-legged robot dog how to play badminton against humans—and it’s not just swiping at the air. This mechanical pup can actually rally, returning shots up to ten times in a row.
At first glance, it sounds like something pulled straight from a quirky sci-fi comedy: a metal dog trotting around a court, swinging a racket. But behind this entertaining image lies serious science. Researchers are testing the limits of how artificial intelligence (AI) and robotics can handle activities that require speed, precision, and coordination—things humans usually excel at.
Meet ANYmal: The Badminton Bot
The star of this unusual experiment is ANYmal, a four-legged robot originally designed for tasks like exploring rough terrain, climbing over obstacles, and even opening doors. Imagine a robotic dog weighing about 110 pounds (50 kilograms) and standing half a meter tall, but built tough enough to handle jobs in environments too dangerous or unpredictable for people.
Researchers have already given robots like ANYmal the ability to perform practical tricks, such as grabbing specific objects or navigating construction sites. But getting them to play a fast-moving sport is a whole new level of challenge. Sports involve quick reactions, sharp vision, and finely tuned body movements—all happening in real time.
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Turning a Robot Dog into a Player
So how do you get a robot to play badminton? The team first gave ANYmal an upgrade: a mechanical arm fitted with a racket, mounted at a 45-degree angle. With this addition, the once squat robot dog suddenly stood taller—about 5 feet 3 inches (1.6 meters)—and had a grand total of 18 movable joints.
To “see” the shuttlecock, ANYmal was fitted with a stereo camera, kind of like a robotic pair of eyes. This allowed it to judge the speed and direction of an incoming shuttlecock. But sight alone isn’t enough; the robot also had to figure out how to move its body in a coordinated way. That’s where reinforcement learning came in.
Learning the Game Through Trial and Error
Reinforcement learning is basically the robotic equivalent of “practice makes perfect.” Instead of being told exactly what to do, the robot learns by trial and error—making attempts, failing, adjusting, and gradually improving.
The team built a virtual badminton court where a digital version of ANYmal practiced for the equivalent of 50 million rallies. Each time it swung at a virtual shuttlecock, the robot earned rewards if it hit the birdie correctly, moved efficiently, or kept its swing precise and on time. This digital boot camp taught ANYmal everything from basic tracking to proper racket positioning.
Once it had mastered the game in its simulated world, the researchers transferred its learned skills to the real robot dog.
From Virtual Court to Real Court
In real-world training, ANYmal faced a bright orange shuttlecock served by a machine. This setup allowed the researchers to control the speed and angle of each shot. ANYmal had to scurry across the court, time its swing, and hit the shuttlecock with enough force to return it over the net.
The results were impressive. After training, the robot was able to return shots at speeds of about 39 feet per second (12 meters per second)—roughly half the strength of an amateur human badminton player. Even more interesting, ANYmal naturally learned to reposition itself back to the center of the court after hitting the shuttlecock, mimicking human strategy without ever being directly taught.
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The Challenges of Robo-Badminton
Of course, teaching a robot dog to play badminton wasn’t without hurdles. One problem was vision. If ANYmal focused too much on looking at the shuttlecock, its movement slowed down. But if it didn’t look closely enough, it risked missing the shot altogether. The AI had to figure out a smart balance—just enough focus to see where the shuttlecock was going, while still moving fast enough to intercept it.
Another issue was coordination. Controlling 18 joints at once is like trying to juggle while riding a unicycle. Each joint has its own motor, learning independently, yet all of them have to move in perfect sync to produce a smooth and accurate swing. The fact that ANYmal figured this out is a sign of how powerful reinforcement learning can be.
Still, there are limits. The robot currently doesn’t account for an opponent’s movements—something human players use to predict where the shuttlecock will land. The researchers suggested future upgrades like adding a neck joint, giving the robot a better field of vision, or teaching it to read an opponent’s body language.
Beyond the Badminton Court
So, is this just a fun party trick for robotics labs? Not quite. While teaching a robot dog to play badminton might sound like an odd choice, it’s actually a clever way to test how machines handle fast-moving, unpredictable environments.
In real life, this kind of skill could prove useful in fields like disaster response. Imagine a robot that can quickly dodge falling debris, spot moving objects in chaotic environments, or even coordinate its limbs to rescue trapped people. Playing badminton may seem trivial, but it requires exactly the kind of real-time decision-making that robots will need in emergencies.
Yuntao Ma, one of the study’s authors, summed it up: sports provide a safe and flexible way to gradually increase difficulty and test robotic intelligence. If a robot can learn badminton rallies today, tomorrow it might learn to handle life-saving tasks with the same agility.
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The Takeaway
ANYmal’s badminton experiment shows just how far robotics has come. A decade ago, watching a robot stumble through basic movements was enough to impress. Now, we’re at the point where robots can run, leap, open doors, and even hold their own in a casual rally of badminton.
The sight of a mechanical dog sprinting across a court with a racket strapped to its arm may seem amusing, but beneath the humor lies a glimpse of the future. As robots become more capable of adapting to dynamic, unpredictable situations, they’ll start stepping out of labs and into everyday roles—from search-and-rescue operations to industrial tasks and maybe even your local sports club.
For now, though, one thing’s certain: if you ever find yourself across the net from ANYmal, you’d better bring your A-game.
Image: GPT-5o.
