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Can humanoid robots move beyond the games?_我的网站

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A Tien Kung robot (left) competes in the 400-meter race at the National Speed Skating Oval in Beijing on August 23, 2026 during the 2nd World Humanoid Robot Games (WHRG). Photo: VCG

On the blue track inside the National Speed Skating Oval, robots chased each other in the 400-meter race. A sprinting Tien Kung robot was tripped by a rival robot lying on the track but did not fall. Instead, it adjusted its posture in seconds and continued toward the finish line with a result of 38.15 seconds, breaking human 400-meter race world record of 43.03 seconds, held by South African athlete Wayde van Niekerk.
This scene unfolded on Sunday, the first official competition day of the 2nd World Humanoid Robot Games (WHRG). Later that evening, a humanoid robot named Lightning, developed by Honor Co., broke the long-standing human 1,500-meter world record of 3:26 set by Moroccan athlete Hicham El Guerrouj with a time of 2:30.22. Just one night earlier, during the opening ceremony's 100-meter sprint and high jump demonstrations, humanoid robots had already shattered records long held by human athletes Usain Bolt and Javier Sotomayor — merely an appetizer for the five-day sci-fi spectacle.
The Games have attracted 666 teams from 16 countries across six continents, up 138 percent from its inaugural edition. The number of participating robots quadrupled to 2,056, the Global Times learned from the organizing committee.
Yet the biggest change this year was not only the scale, but also the technological maturity. More teams have shifted from remote-controlled robots to fully autonomous systems. Chinese observers, after watching several events, said humanoid robots are no longer limited to simple demonstrations of walking or dancing. Increasingly, they are being tested in environments that demand speed, coordination, perception and decision-making.
From movement to intelligence

Behind the record-breaking performances are a series of technological upgrades. During the running events, Global Times reporters observed that robots were able to make subtle movements around their hip joints while walking, allowing them to maintain balance and produce movements closer to human biomechanics.
Guo Yijie, technical director of Tien Kung Ultra, said the improvement came from advances in hardware design.
"Robot joints have improved in torque and speed. As competitions demand higher power output, the reliability of the main control board, power board and high-power battery discharge capability have become increasingly important," Guo told the Global Times.
According to Guo, the same Tien Kung Ultra robot was used in both sprinting and jumping events. For running, the waist needs rotation to balance the inertia generated by leg movements. For jumping, the upper body requires pitching movement to help the robot achieve greater distance or height, Guo said, noting that these can all be achieved with only slight adjustments to the waist configuration were needed, and it highly demonstrated to the robot design's flexibility.
Besides the hardware upgrades, developments in robots' faster decision-making in "brains' is better showcased in football competitions.
The Booster T2 robot used by many domestic and overseas football teams has increased its onboard computing capability by 10 times compared with the previous generation. The time from recognizing the goal to deciding whether to pass or shoot has been reduced to around 100 milliseconds - faster than the time humans usually need to blink.
Cheng Hao, a technical representative from Boosters Robotics, said the improvement represents an "end-to-end" transformation in embodied intelligence.
"In the past, robots relied on separate steps: perception, planning, decision-making and execution. Each stage was disconnected. Now, with an end-to-end motion foundation model, visual input can directly generate joint control commands," Cheng said.
The newly introduced table tennis and tennis competitions are also testing another important capability: perception.
"Robots are moving from simply recognizing the three-dimensional position of a ball to understanding the movement information of opponents and more complex environments," said Li Yinghui, a postdoc researcher and the leader from the Hong Kong University team at the table tennis event. Such capabilities developed in sports scenarios could eventually be transferred to other scenarios in industries or daily life, Li said.
Beyond entertainment
Though clips from the robot competitions have made a buzz on social media, with some critics from Western media questioning why robots need to dance, play football or compete in athletics while arguing that these activities appear far from everyday needs.
Zhou Changjiu, Vice President of the RoboCup Federation and the Robotics Competition Association of the Asia-Pacific (RCAP), stressed robot sports are not designed for entertainment alone. "Robot competitions are testing the capabilities needed for humanoid robots to enter the real world," Zhou told the Global Times.
Different events evaluate different abilities. Sprinting tests speed, marathons examine battery endurance and energy management, while football provides a comprehensive test because of its dynamic and unpredictable nature, testing intelligence, manipulation, interaction, safety, cost and trust at the same time, Zhou said.
Unlike a single-task demonstration, football requires robots to cooperate. Players must understand their own roles, recognize teammates and opponents, and make collective decisions - a form of swarm intelligence that could become essential when robots work together in homes, factories and emergency rescue environments.
Entertainment, however, can generate business opportunities. Gu Jinghai, brand director of EYOU Robot Co, told the Global Times that the company sold 95,000 integrated joints in China in 2025 and expects sales to exceed 1 million units in 2026. Growth mainly comes from planetary joints used in entertainment robots, while harmonic joints for industrial robots are also expanding steadily, he said.
Jiao Jichao, an executive from UBTECH Robotics, said the criticism that "Chinese robots can only sing and dance" reflects only a temporary stage of development. "Performance and dancing are also real commercial demands, and their existence has value," Jiao said. "Beyond entertainment, Chinese robots are already demonstrating intelligent capabilities that support practical applications in manufacturing, and partnerships with companies including Huawei and Foxconn, where robots have already entered production lines in small-scale applications."
The Outline of the 15th Five-Year Plan for National Economic and Social Development of China stated that the country will construct a comprehensive cultivation system for future industries including the embody intelligence as new economic growth drivers.
Zhou said the development of humanoid robots is not only about individual machines, but about building an entire ecosystem.
China's advantage lies in its complete supply chain, abundant application scenarios and manufacturing capabilities, while the fast growing industry still faces challenges such as the lack of unified standards.
Gu said the company has launched mass production of robotics joint under domestic standard of auto parts.
Robot competitions can help address these issues by creating common standards in areas such as competition rules, robot dimensions, communication systems, batteries and joint modules. "As applications expand, the cost of key robot components will continue to decline," Zhou said.
"Numbers are magic," Zhou said. "If producing one robot costs a huge amount because it is handmade, producing one million robots will completely change the cost structure."
For humanoid robots, the race taking place inside the Ice Ribbon may not ultimately be about who crosses the finish line first. It may be about whether these machines can learn to navigate the far more complicated race outside the stadium - the race into the real world.
。 IT之家 7 月 21 日消息，科技媒体 The Decoder 昨日（7 月 20 日）发布博文，报道称谷歌 DeepMind 发布 GenCeption 模型，将预训练的视频生成器重新用于深度估计和分割等经典计算机视觉任务。 IT之家援引博文介绍，大语言模型在学习预测下一个 Token 的时候，在训练过程中往往需要吸收语法、世界知识和上下文关系等内容。但是在计算机视觉领域，视觉模型缺少等效的训练方法，主要由专业模型主导，包括用于分割的“Segment Anything”和用于深度估计的“Depth Anything”，每个模型都使用其特定的架构。谷歌 DeepMind 团队为此提出 GenCeption 模型方案，尝试将一个“生成视频”的 AI 模型逆向改造成一个能“理解世界”的视觉分析引擎。

二 | GenCeption 打破了传统计算机视觉“一个任务一个专用模型”的格局，仅凭单一模型就能同时做好深度估计、图像分割、3D 姿态估计、表面法线预测和相机姿态估计等核心视觉任务。

三 | GenCeption 基于阿里巴巴开源视频模型通义万相 Wan2.1 系列训练，与传统扩散模型需多步去噪不同，GenCeption 在一次前向传播中完成预测，从而提升视觉任务处理速度。

四 | 模型通过文本提示指定任务，可输出深度图、表面法线图、分割掩码，并可处理相机运动表示。训练数据以合成为主。

五 | 论文称，数据集仅包含 7500 段视频，由 800 个数字人体模型与 200 段动作捕捉序列组合生成，再通过 Blender 在不同背景和镜头角度下渲染。泛化方面，GenCeption 几乎只在单人合成视频上训练，但可处理真实多人视频，也可迁移到动物和类人机器人类别。论文称，部分输出细节甚至超过训练时 Blender 渲染结果，可保留猫胡须和单根发丝边缘。