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操作员不再必须遥控一台真正的机器人，而是戴上第一视角设备、腕部设备，或直接拿着夹爪完成抓取、搬运和整理。8月31日，智元机器人旗下觅蜂科技宣布，第2万套MEgo系列设备下线，并发布了100万小时无本体数据集。这批数据来自不同人的操作，不绑定某一种机器人本体，意味着那些过去没有被记录的物理经验，开始进入规模化生产。
Operators no longer have to remotely control a real robot. Instead, they can wear first-person-view equipment or wrist-mounted devices, or directly hold a gripper to grasp, move and organize objects. On August 31, Mifeng Technology, a subsidiary of AgiBot, announced that its 20,000th MEgo-series device had rolled off the production line and released a 1-million-hour embodiment-free dataset. The data comes from people performing different tasks and is not tied to any particular robot embodiment, meaning that physical experience that previously went unrecorded is beginning to enter large-scale production.
数据的覆盖面同样被摆上台面：22大类场景、500余种任务类型、超过1万个真实场景和5万余个物体类别。其中，50万小时来自裸手采集，35万小时来自腕部采集，15万小时来自夹爪采集。觅蜂科技今年2月成立，董事长兼首席执行官姚卯青称，通往通用人工智能，物理人工智能最终可能需要上千万、上亿小时数据，但今天只是起点。
The data's breadth is also being put on display: 22 broad scenario categories, more than 500 types of tasks, over 10,000 real-world scenarios and more than 50,000 object categories. Of this total, 500,000 hours came from bare-hand collection, 350,000 hours from wrist-mounted collection and 150,000 hours from gripper collection. Mifeng Technology was founded in February this year. Its chairman and CEO, Yao Maoqing, said that achieving artificial general intelligence and ultimately physical AI may require tens of millions or hundreds of millions of hours of data, but that today is only the starting point.
这条路首先解决的是“怎么更便宜地获得数据”。京东科技集团副总裁何贺回忆，传统遥操作让操作员工作8小时，最终可能只有1小时有效数据，还要承担机器人、电费、人工和场地成本；正常工作时佩戴设备，8小时最多可以产生5—6小时数据。数据生产效率提高了，训练材料的供给也随之扩大，但一小时数据里究竟有多少真正有价值的动作，仍不能只看时长。
The first problem this approach addresses is “how to obtain data more cheaply.” He He, vice president of JD Technology, recalled that traditional teleoperation requires an operator to work for eight hours but may ultimately produce only one hour of usable data, while also incurring the costs of the robot, electricity, labor and facilities. Wearing equipment during normal work, by contrast, can generate as much as 5—6 hours of data in eight hours. Data-production efficiency has improved, expanding the supply of training material, but the amount of genuinely valuable action in one hour of data cannot be judged by duration alone.
具体来说，百万小时并不等于机器人已经变聪明了同样多。语言模型拥有网页、书籍、代码和视频等现成数字材料，而拿杯子、叠衣服、把不同材质物品放进货架等经验并不会自然出现在互联网上。可是，不同机器人的夹爪、自由度、尺寸、力矩和控制频率各不相同，人完成的动作不能直接变成每台机器人的控制信号；无本体数据主要用于预训练和通用表征，具体机器人落地仍需要真机数据。
More specifically, 1 million hours does not mean that robots have become equally more intelligent. Language models have ready-made digital material in the form of webpages, books, code and videos, but experience such as picking up a cup, folding clothes or placing objects made of different materials on a shelf does not naturally appear on the internet. However, the grippers, degrees of freedom, dimensions, torque and control frequencies of different robots vary, so actions performed by humans cannot be directly converted into control signals for every robot. Embodiment-free data is mainly used for pretraining and general representation learning; deploying a specific robot still requires real-robot data.
对工厂、仓库、商场和未来家庭场景而言，眼下最实际的变化是数据采集不再只能依赖昂贵的单机遥操作。更丰富的环境、物体和任务组合，理论上有机会帮助机器人学习更通用的规律；但觅蜂科技此次展示的主要是数据规模、覆盖范围以及采集和治理能力，尚未公布百万小时数据能让某个具体模型获得多少能力增益。数据产能的工业化已经发生，智能能力是否随之涌现，还要由模型训练和真实部署回答。
For factories, warehouses, shopping malls and future household settings, the most practical change for now is that data collection no longer has to rely solely on costly, single-robot teleoperation. In theory, a richer combination of environments, objects and tasks could help robots learn more general rules. But what Mifeng Technology demonstrated this time was mainly the scale of the data, its coverage, and its collection and governance capabilities. It has not yet disclosed how much capability gain the million hours of data can deliver for any specific model. Industrialization of data-production capacity has already taken place; whether intelligent capabilities will emerge alongside it remains for model training and real-world deployment to answer.