Watch Sony’s elite ping-pong robot beat top-ranked players

2026-04-22 · Source: The Verge · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Intermediate, quick

Summary

Sony's AI division has developed Ace, a robotic table tennis player capable of competing against and occasionally defeating top-ranked human players under official International Table Tennis Federation (ITTF) rules. Unlike previous ping-pong robots like Omron's FORPHEUS, Ace is designed to match human speed and responsiveness in physical games. The robotic system features eight joints: two for paddle position, two for orientation, and three for powerful shots. Its sophisticated vision system uses nine traditional cameras for 3D ball location and three "gaze control systems" to measure angular velocity and spin for trajectory calculation. In test matches in April 2025, Ace won three out of five matches against elite players and later defeated professional players in December 2025 and last month.

Key takeaway

For robotics engineers developing AI for dynamic physical environments, Ace's success highlights the importance of integrating advanced multi-joint robotic systems with sophisticated vision and trajectory prediction. You should consider how a combination of precise mechanical control and real-time sensory input can enable AI to perform complex, high-speed tasks, potentially informing designs for manufacturing, logistics, or even surgical robotics.

Key insights

Sony's Ace robot demonstrates AI's capability to excel in complex physical sports like table tennis against top human players.

Principles

• Physical AI requires matching human speed and responsiveness.
• High-speed ball sports demand precise trajectory prediction.

Method

Ace employs an eight-joint robotic system for paddle control and shot delivery, combined with a multi-camera vision system for 3D ball tracking, angular velocity, and spin measurement to predict trajectory.

In practice

• Integrate multi-joint robotics for agile physical tasks.
• Utilize multi-camera vision for high-speed object tracking.

Topics

• Sony Ace
• Ping-pong Robotics
• AI Vision System
• Robotic Control System
• Table Tennis AI

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Robotics Engineer, Tech Journalist

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Verge.