Reinforcement learning enables robot to master badminton
The AI robot predicts shuttlecock movement and returns shots in real time.
A Swiss-led team at ETH Zurich has developed an AI-powered legged robot capable of playing badminton against human opponents with impressive precision and agility.
The project uses reinforcement learning, a type of AI that enables the robot to refine its movements and decisions through repeated trial and error.
The robot can accurately track the shuttlecock, predict its trajectory, and position itself effectively to return shots during high-speed rallies. Its ability to navigate the court and respond in real-time demonstrates significant progress in applying AI to dynamic, physical tasks.
Lead researcher Yuntao Ma said the project highlights the potential for AI to drive legged robots in increasingly complex activities.
The work represents a step forward in developing autonomous and intelligent robotic systems, including future humanoids capable of interacting in real-world environments.
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