Humanoid Robot Wins Beijing Half-Marathon, Posts Time Faster Than Human World Record

(MENAFN- Live Mint) A humanoid robot developed by Honor has captured global attention after winning a half-marathon race for robots in Beijing, posting a time that surpasses the human world record for the distance. The event, held in the Beijing Economic-Technological Development Area (E-Town), highlights China's accelerating progress in robotics and artificial intelligence.

The winning robot completed the 21-kilometer (13-mile) course in 50 minutes and 26 seconds. This time is notably faster than the current human half-marathon world record held by Jacob Kiplimo, who ran the distance in approximately 57 minutes at a Lisbon road race in March 2026.

A separate robot from Honor, operating via remote control, crossed the finish line even faster at 48 minutes and 19 seconds. However, under the competition's weighted scoring system-which prioritized autonomy-the fully autonomous robot was awarded first place.

Marked improvement over previous year

The 2026 race demonstrated dramatic progress compared to the inaugural event in 2025, where the winning robot recorded a time of 2 hours, 40 minutes, and 42 seconds. This year's results represent a significant leap in both speed and operational efficiency.

Autonomy vs remote control

Approximately 40% of participating robots navigated the course autonomously, while the remaining machines relied on remote human control. The runner-up robots, also developed by Honor and operating autonomously, finished in 51 minutes and 53 minutes, respectively.

The race format emphasized real-world robotic capabilities, including navigation, obstacle avoidance, and endurance-key benchmarks for future deployment in logistics, emergency response, and urban services.

Challenges during the race

Despite the impressive results, the competition was not without setbacks. Some robots encountered operational difficulties: one fell at the line, while another collided with a barrier during the race. These incidents underscore the ongoing challenges in achieving fully reliable humanoid mobility in dynamic environments.

(With AP inputs)

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