AI-Powered Robot Masters Badminton with Advanced Skills
▼ Summary
– Robots like Atlas and Spot lacked quick perception-to-movement reflexes, similar to human reflexes for tasks like catching a ball.
– ETH Zurich scientists, led by Yuntao Ma, developed an AI-powered badminton-playing robot named ANYmal to address this gap.
– ANYmal, a quadruped robot resembling a miniature giraffe, was built by ANYbotics and featured elastic actuators, weighing 50 kg and measuring under a meter in length.
– The robot was equipped with a flexible arm from Duatic to hold a racket and a stereoscopic camera for tracking shuttlecocks and sensing its environment.
– Instead of traditional model-based control, the team used reinforcement learning, allowing the robot to learn movements independently in a simulated environment.
Cutting-edge robotics research has achieved a remarkable breakthrough by developing an AI-powered robot capable of playing badminton with human-like reflexes. Scientists at ETH Zurich have successfully bridged the gap between perception and movement in robotics, creating a system that responds to dynamic environments with unprecedented speed and precision.
The project, led by roboticist Yuntao Ma, focused on integrating real-time perception with physical agility—a challenge that has long hindered robotic development. “Our goal was to merge sensory input with fluid motion, mimicking the instinctive reactions humans use in sports,” Ma explained. The team modified an industrial-grade quadruped robot called ANYmal, originally designed for oil and gas inspections, transforming it into a badminton-playing machine.
Standing at just under a meter tall and weighing 50 kilograms, the robot resembles a compact, four-legged creature gripping a racket in its mouth. Its legs feature elastic actuators for stability, while a custom-built robotic arm, developed by ETH Zurich spinoff Duatic, provides the dexterity needed to swing the racket. A stereoscopic camera tracks the shuttlecock’s trajectory, allowing the system to react in milliseconds. “This hardware integration took five years of refinement,” Ma noted.
Unlike traditional robotics that rely on complex mathematical models, the team employed reinforcement learning, a method where the AI trains itself through simulated trial and error. “Instead of programming every movement, we let the robot learn naturally in a virtual environment,” Ma said. This approach enabled the robot to develop adaptive strategies, adjusting its swings and footwork dynamically during play.
The breakthrough demonstrates how AI can enhance robotic agility beyond pre-programmed tasks, opening doors for applications in sports, search-and-rescue operations, and industrial automation. While still in development, the project highlights the potential for robots to perform in unpredictable, real-world scenarios with human-like responsiveness.
(Source: Ars Technica)
