在经历了多年制造能够奔跑、跳舞和完成后空翻的人形机器人后，中国企业正将资源倾注于另一个挑战：开发能让这些机器在更广泛工作中发挥作用的人工智能。
After years of building humanoid robots capable of running, dancing and performing backflips, Chinese companies are pouring resources into a different challenge: developing the AI that could make those machines useful across a wider range of jobs.
记者 Laurie Chen、Eduardo Baptista 报道 这一转变正推动具身智能领域的投资热潮，该领域专注于让机器人理解周围环境、学习陌生任务并能在无需为每项工作单独编程的情况下适应调整的软件开发。
By Laurie Chen, Eduardo Baptista The shift is fuelling an investment boom in embodied AI, a field focused on software that allows robots to understand their surroundings, learn unfamiliar tasks and adapt without having to be programmed for every job.
据数据研究机构 IT桔子 统计，2026年上半年，中国具身智能企业在322笔交易中共融资935亿元人民币（约合130亿美元），是去年同期的五倍。资金正越来越多地流向机器人智能专业开发商以及制造商。
Chinese embodied AI companies raised 93.5 billion yuan ($13 billion) across 322 deals in the first half of 2026, five times the amount a year earlier, according to data research firm ITjuzi. Funding is increasingly reaching specialist developers of robot intelligence alongside manufacturers.
随着竞争压低硬件价格，总部位于上海的科技咨询公司 ZenGen Labs 创始人 Dermot McGrath 表示，机器人制造商已“得出结论，利润不会来自硬件，而将来自大脑”。
As competition drives down hardware prices, robot makers have "concluded that the margin won't come from the hardware but from the brain," said Dermot McGrath, founder of Shanghai-based technology consultancy ZenGen Labs.
这一新兴领域正在中美科技竞争中开辟新战场。美国企业在某些先进机器人AI系统方面处于领先地位，而据路透社对五家中国机器人企业的创始人及高管、以及行业分析师的采访，中国开发者希望凭借制造优势和真实世界训练数据的获取渠道，对美国竞争对手发起挑战。
The emerging sector is opening a new front in US-China tech rivalry. American companies lead in some advanced robot AI systems, while Chinese developers hope their manufacturing strength and access to real-world training data will help them challenge their US competitors, according to Reuters interviews with founders and top executives at five Chinese robotics firms, as well as industry analysts.
最能将AI与机器结合的国家，有望在工厂和物流自动化方面获得优势，从而可能重塑工业生产力和全球机器人技术市场。
The country that best combines AI with machines could gain an advantage in automating factories and logistics, potentially reshaping industrial productivity and the global market for robotics technology.
“在完整的机器人技术栈中，大脑确实是最薄弱的一环，”Spirit AI 联合创始人高扬表示。
"The brain is indeed the weakest link in the complete robotics stack," said Spirit AI co-founder Gao Yang.
美国初创公司 Physical Intelligence、Skild AI 和 Generalist 正在开发软件，旨在让机器人在无需大量再训练的情况下掌握不同技能。
American startups, including Physical Intelligence, Skild AI and Generalist, are developing software intended to teach robots different skills without extensive retraining.
Generalist 表示，其 GEN-1.5 模型在观看时长 3 至 12 秒的演示后，完成了约 60% 的简单任务。中国的 X Square 发布研究显示，机器人仅凭单个人类视频即可习得操作技能，无需针对每个任务重新训练模型。
Generalist says its GEN-1.5 model completed around 60% of simple tasks after viewing a demonstration lasting between 3 and 12 seconds. China's X Square has published research showing robots acquiring handling skills from a single human video without retraining the model for each task.
“美国领先实验室正在树立一个要求很高的基准，特别是在机器人习得和复用技能的速度方面，”机器人顾问 Georg Stieler 表示，他认为中国最强的初创企业是可信的竞争对手。
"The leading American labs are setting a demanding benchmark, particularly in how quickly robots can acquire and reuse skills," said robotics consultant Georg Stieler, who considers China's strongest startups credible competitors.
虽然美国开发者受益于先进的 AI 研究和更容易获得强大的算力芯片，但中国公司能与机器人制造商和工业客户紧密合作，以测试和改进其系统。
While US developers benefit from advanced AI research and greater access to powerful computing chips, Chinese companies can work closely with robot manufacturers and industrial customers to test and improve their systems.
据公司披露，包括 X Square、Galaxea 和 Spirit AI 在内的中国专业初创企业，估值均已达到约 30 亿美元。
Specialist Chinese startups, including X Square, Galaxea and Spirit AI, have each reached valuations of roughly $3 billion, according to company disclosures.
“我们希望机器人从‘代码驱动’转向‘常识驱动’，”X Square 联合创始人兼首席运营官杨倩表示。
"We want robots to shift from being driven by code to being driven by common sense," said X Square co-founder and COO Yang Qian.
老牌制造商也在投入资源。香港上市的 优必选 发布首席科学家招聘广告，负责统筹 AI 模型开发，薪酬最高可达 1.24 亿元人民币（约 1800 万美元），含股权和福利。总部位于杭州的 宇树科技 是按出货量计算全球最大的人形机器人制造商之一，据一位知情人士透露，该公司约有 30 名研究人员正在开发能预测物体行为的 AI。
Established manufacturers are investing too. Hong Kong-listed UBTech advertised a chief scientist position overseeing AI model development with compensation of up to 124 million yuan ($18 million), including equity and benefits. Hangzhou-based Unitree, one of the world's largest humanoid robot makers by shipments, has about 30 researchers developing AI that predicts how objects behave, according to a person briefed by the company.
与从海量互联网文本中学习的聊天机器人不同，机器人需要物理交互的范例，从抓取物体到处理柔性线缆。
Unlike chatbots, which learn from vast quantities of internet text, robots need examples of physical interactions, from gripping objects to handling flexible wires.
中国公司正采取不同途径收集这些信息，有的雇佣人员演示任务，有的使用计算机仿真，也有的两者结合。该领域竞争依然拥挤，尚无公司确立绝对领先地位。
Chinese companies are pursuing different approaches to collecting that information, with some employing people to demonstrate tasks, others using computer simulations, and some combining both. The field remains crowded, with no company establishing itself as the clear leader.
Spirit AI 已雇佣约 1000 人记录日常和工业活动，高某表示。ACE Robotics 利用可穿戴传感器采集商店和酒店工人的动作与握力数据。
Spirit AI has hired around 1,000 people to record everyday and industrial activities, Gao said. ACE Robotics uses wearable sensors to capture workers' movements and grip forces in stores and hotels.
蚂蚁集团旗下机器人业务单元 Robbyant 称，其使用 16 万小时素材训练模型，其中包括 5 万小时人类引导机器人运动的数据。该公司还在 17 个品牌的 20 种机器人配置上训练软件，旨在让同一套 AI 能在不同机器上运行。
Ant Group's robotics unit Robbyant said it trained a model using 160,000 hours of material, including 50,000 hours of human-guided robot movements. It also trained software on 20 robot configurations from 17 brands, aiming to use the same AI across different machines.
DexForce 采取另一条路径，利用计算机生成的环境训练机器人。首席运营官 Nicole Yang 表示，公司可在 6 至 8 小时内为部分工业任务完成部署准备，并在特定精密操作中实现 99.9% 以上的成功率。
DexForce is taking another approach, using computer-generated environments to teach robots. Chief operating officer Nicole Yang said the company can prepare some industrial tasks for deployment within 6 to 8 hours and reported success rates above 99.9% in specified precision operations.
但空间智能公司 Manycore Tech 的 Jerry Wang 指出，仿真无法完美复现物理交互，该公司专门为 AI 训练设计仿真环境。
But simulations cannot perfectly reproduce physical interactions, said Jerry Wang of spatial intelligence company Manycore Tech, which designs simulated environments for AI training.
“数据质量比数据多样性更重要，也比单纯大规模收集数据更重要，”X Square 的杨某表示。
"Data quality is more important than data diversity, and more important than simply collecting data at massive scale," X Square's Yang said.
中国机器人企业还面临额外逆风，包括因美国出口管制导致的高端英伟达芯片短缺，以及在监管层对估值虚高和赛道拥挤的担忧下，IPO 融资受到挤压。
Chinese robot firms also face additional headwinds, including shortages of advanced Nvidia chips due to US export controls, and a squeeze on IPO funding amid regulatory concerns over inflated valuations and overcrowding.
JoyIn Technology Aether 机器人 AI 开发负责人朱明轩表示，随着机器人变得更强大，高端芯片短缺将成为中国具身智能产业的严重问题。
As robots become more powerful, the shortage of advanced chips will become a serious problem for China's embodied AI industry, said Zhu Mingxuan, who leads development of JoyIn Technology's Aether robot AI.
JoyIn 等开发者已在测试其软件能否让机器人应对陌生问题。
JoyIn and other developers are already testing whether their software can enable robots to tackle unfamiliar problems.
朱明轩描述了一个实验：机器人反复尝试将耳机放入够不着的柜子，均以失败告终。
Zhu described an experiment in which a robot repeatedly failed to place headphones in a cupboard beyond its reach.
随后工程师向它展示人类完成该任务的视频，机器人便推动一个箱子靠近柜子，并尝试踩上去，朱明轩说。“每一次它都在探索不同的行为，”她说。
Then engineers showed it videos of how people managed the task, and the robot pushed a box towards the cupboard and tried stepping onto it, Zhu said. "Every time it was exploring a different behaviour," she said.
中国的实际部署不容许即兴发挥。电池制造商宁德时代表示，Spirit AI机器人在其中州工厂的电池包测试中连接柔性导线，插入成功率超过99%。Robbyant（若贝特）表示，其机器人在上海两家药店处理订单，员工在发货前核对药品。
Real-world deployments in China leave no room for improvisation. Battery maker CATL said Spirit AI robots connect flexible wires during battery-pack testing at its Zhongzhou plant, reporting insertion success above 99%. Robbyant said its robots handle orders in two Shanghai pharmacies, with staff checking medicines before dispatch.
但长远来看，真正的奖赏在于无需大量适配即可学会新工作的软件。成败将取决于同一个AI在不同客户和工作场所的表现，这将要求企业利用先进芯片训练日益强大的模型。
But the longer-term prize is software capable of learning new jobs without extensive adaptation. Success will depend how the same AI performs across different customers and workplaces and that will require companies to train increasingly powerful models with advanced chips.
中国企业正试图通过更高效的模型、合成数据和仿真来降低算力需求，同时包括华为在内的国产芯片制造商正在开发英伟达硬件的替代品。这些努力能在多大程度上弥补短缺仍不清楚。
Chinese companies are trying to reduce their computing requirements through more efficient models, synthetic data and simulations, while domestic chipmakers, including Huawei, are developing alternatives to Nvidia hardware. It remains unclear how far those efforts can offset the shortage.
优必选创始人王兴兴本月表示，公司已在机器人上测试了OpenAI的GPT-6，结果令人鼓舞。
Unitree founder Wang Xingxing said this month the company had tested OpenAI's GPT-6 on robots with encouraging results.
他说，如果GPT-7明年发布，可能为机器人领域带来属于自己的“ChatGPT时刻”——即能力的阶跃式提升，类似于ChatGPT于2022年发布后，将生成式AI变成了大众消费和商业技术。
He said GPT-7, if released next year, could bring robotics its own "ChatGPT moment" — a step-change in capability akin to ChatGPT's 2022 launch, which turned generative AI into a mainstream consumer and business technology.
王兴兴表示，当机器人能凭口头指令在80%的陌生环境中完成80%的任务时，他会认为达到了这一门槛。
Wang said he would consider that threshold reached when robots were able to complete 80% of tasks in 80% of unfamiliar environments from spoken instructions.
但顾问Stieler表示，演示中的突破不一定意味着机器人已准备好大规模商用。
But consultant Stieler said a breakthrough in demonstrations would not necessarily mean robots were ready for broad commercial use.
“如果问题是模型何时首次能在演示中处理广泛的陌生任务，乐观主义者可能是对的，”他说。“如果问题是客户何时能依赖它，我会说要加年头，不是月份。”
"If the question is when a model first handles a broad range of unfamiliar tasks in demonstrations, the optimists may well be right," he said. "If the question is when a customer can count on it, I would add years, not months."