{"id":561373,"date":"2026-09-01T06:24:20","date_gmt":"2026-09-01T06:24:20","guid":{"rendered":"http:\/\/www.montpelierjournal.com\/news\/story\/561373\/chingmu-powers-the-world-humanoid-robot-games-whrg-2026-with-highprecision-motion-capture.html"},"modified":"2026-09-01T06:24:20","modified_gmt":"2026-09-01T06:24:20","slug":"chingmu-powers-the-world-humanoid-robot-games-whrg-2026-with-highprecision-motion-capture","status":"publish","type":"post","link":"https:\/\/www.nebraskanewsdesk.com\/news\/story\/561373\/chingmu-powers-the-world-humanoid-robot-games-whrg-2026-with-highprecision-motion-capture.html","title":{"rendered":"CHINGMU Powers the World Humanoid Robot Games (WHRG 2026) with High-Precision Motion Capture"},"content":{"rendered":"

Shanghai, China – September 1, 2026 –<\/strong> Robots playing tennis, sprinting 100 meters, performing cheerleading, tackling complex scenario tasks, and executing dexterous manipulations — the 2nd World Humanoid Robot Games concluded on August 26 at Beijing’s National Speed Skating Oval . With 666 teams and 2,056 robots from 16 countries competing across a series of high-dynamic, high-difficulty events, humanoid robots’ athletic capabilities once again took center stage.<\/p>\n

Behind these spectacular moments lies a critical yet often overlooked data pipeline.<\/p>\n

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As the motion capture technology provider for WHRG 2026, CHINGMU deployed high-precision optical systems to capture sub-millimeter positioning, tracking, and motion data across venues.<\/strong>The systems translate robots’ real-time positions, trajectories, and posture changes into recordable, analyzable data — laying a high-precision foundation for motion documentation, post-event performance analysis, and quantitative evaluation.<\/p>\n

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Part 01<\/strong>|<\/strong>The Better Robots Move, the More They Need a High-Precision “Ruler”<\/strong><\/p>\n

At the opening ceremony, Galbot’s “AstraTennis Moment” became the highlight of the night — a humanoid robot played tennis alongside Grand Slam champion Zheng Jie and “experience officer” Lin Yunrui, executing forehands, backhands, serves, returns, and baseline movements. This high-speed ball sport vividly demonstrated the robot’s full-body coordination, dynamic response, and motion control to the public. Robot table tennis, showcased alongside the tennis match, also drew significant attention both on and off the court.<\/p>\n

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These high-dynamic scenarios raise a fundamental question: as robots become increasingly athletic, how do we accurately record and evaluate their performance?<\/strong><\/p>\n

In high-speed ball sports, the spatial relationships among the ball, racket, robot body, court, and opponent are constantly shifting. Simply knowing “whether the ball went over” is far from enough — was the trajectory accurate? Was the body posture stable? How much did the execution deviate from the intended motion?<\/p>\n

These questions, invisible to the naked eye, can only be answered through data.<\/p>\n

As an independent measurement layer beyond a robot’s onboard sensing, optical motion capture systems provide high-precision spatial localization and motion tracking of robots, humans, and balls from an external perspective — delivering a unified measurement benchmark and high-accuracy ground truth for motion analysis and performance validation.<\/p>\n

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If a robot’s own sensors help it perceive the world, an external high-precision motion capture system acts as an independent “spatial ruler” — it doesn’t make decisions for the robot; it accurately records how the robot actually moves.<\/p>\n

Part 02<\/strong>|<\/strong>From “Looks Good” to “Proven by Data”<\/strong><\/p>\n

This year’s Games featured multiple scenario-based events, dexterous hand competitions, and a pentathlon<\/strong>— all designed to mirror real-world application environments. From fine manipulation to full-body motion, the events comprehensively tested robots’ perception, decision-making, control, and execution capabilities.<\/p>\n

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Tailored to each scenario, CHINGMU deployed hybrid marker-based and markerless motion capture solutions. Using K Series, MC Series, and R3 motion capture cameras paired with CMAvatar software, the systems delivered high-precision positioning, tracking, and motion data acquisition for robot movements.<\/p>\n

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Many events featured fully autonomous robot operation. The motion capture systems deployed on-site did not participate in robot control or decision-making; instead, as independent external measurement tools, they converted real motion into computable data — position, posture, trajectory, velocity, stability, and more.<\/strong><\/p>\n

A standard camera records “what happened”; a motion capture system records “how exactly it happened.” When motion is quantified into objective metrics — trajectory deviation, posture variation, stability — problems once judged only by experience gain a data foundation for review, validation, and algorithm optimization.<\/p>\n

Part 03 | From Learning to Validating: How High-Quality Motion Data Drives Robot Evolution<\/strong><\/p>\n

The value of motion capture extends far beyond post-event review. In robot R&D, an increasingly clear data pipeline is taking shape:<\/p>\n

Real-world motion → High-quality motion data → Robot training & optimization → Real-machine execution → External measurement & validation → Data feedback loop<\/strong><\/p>\n

High-dynamic ball sports are among the most tangible applications of this pipeline.<\/p>\n

When facing a fast-flying, rapidly changing table tennis ball, robots need not only quick reflexes but also high-precision 3D spatial information. CHINGMU’s high-performance optical motion capture systems enable real-time 3D localization and tracking of both the high-speed ball and the robot body, capturing ball trajectory, velocity, landing point, and robot spatial position — providing high-accuracy data to support motion control, analysis, and performance validation.<\/p>\n

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CHINGMU has already applied these capabilities across multiple public robotics projects:<\/p>\n