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8月31日，觅蜂科技董事长兼首席执行官姚卯青把第2万套MEgo无本体数据采集设备交到京东手中。设备将进入京东真实业务场景使用。觅蜂科技同时宣布，已累计生产超过100万小时无本体数据，并开始向腾讯Robotics X实验室、蚂蚁灵波等客户提供数据服务。
On August 31, Mifeng Technology Chairman and CEO Yao Maoqing handed JD.com its 20,000th MEgo body-free data-collection device. The device will be used in JD.com’s real-world business scenarios. Mifeng Technology also announced that it had produced more than 1 million hours of body-free data and had begun providing data services to customers including Tencent’s Robotics X laboratory and Ant Lingbo.
MEgo记录的不是单一画面。它用超过300°的全景视野加腕部特写，同时采集RGB、深度、惯性测量、触觉和音频信息；三维触觉配合毫米级轨迹重建，硬件级同步则把各通道数据对齐到亚毫秒级。人的手部、身体、头部、机器人末端和周围环境，由此被转化为统一的空间数据。
MEgo does not record a single image stream. It combines a panoramic field of view of more than 300° with wrist close-ups, while simultaneously capturing RGB, depth, inertial-measurement, tactile and audio information. Three-dimensional tactile sensing works with millimeter-level trajectory reconstruction, while hardware-level synchronization aligns data from each channel to the sub-millisecond level. A person’s hands, body and head, along with the robot’s end effector and the surrounding environment, are thereby converted into unified spatial data.
这些数据来自开放环境中的真实采集，覆盖22大类场景、10,000余个真实环境、50,000余类物体和500余种细分任务，从居住、办公、零售、生产、仓储、餐饮、文娱休闲、交通出行，延伸到医疗、教育、养老和农业生产等长尾场景。觅蜂披露，八大头部场景贡献约82%的数据，其中Ego裸手约50万小时、Ego+Wrist约35万小时、Ego+UMI夹爪约15万小时。
The data comes from real-world collection in open environments and covers 22 major scenario categories, more than 10,000 real-world environments, more than 50,000 types of objects and more than 500 specific tasks. These range from residential, office, retail, production, warehousing, dining, entertainment and leisure, and transportation scenarios to long-tail applications in healthcare, education, elderly care and agricultural production. Mifeng said that eight leading scenario categories account for approximately 82% of the data, including around 500,000 hours of bare-hand Ego data, 350,000 hours of Ego+Wrist data and 150,000 hours of Ego+UMI gripper data.
为处理这批数据，觅蜂搭建了MEgo Engine，覆盖处理、感知、标注和质量评估，并发布HandPose高精度手部重建技术。配套的ManiEval体系从数据类型、任务类型、传感器质量、语义质量和动作质量等维度分级，采取“不过滤，只分级”的方式，把不同质量的数据匹配给不同的模型训练需求。
To process this data, Mifeng built MEgo Engine, which covers processing, perception, annotation and quality assessment, and released its high-precision HandPose hand-reconstruction technology. The accompanying ManiEval system grades data across dimensions including data type, task type, sensor quality, semantic quality and action quality. Its approach is to “not filter, only grade,” matching data of different quality levels to different model-training needs.
具体改变是，机器人模型训练开始获得更连续、更接近真实工作的数据供给：京东拿到的是一套即将进入业务现场的采集设备，腾讯Robotics X实验室和蚂蚁灵波则已成为数据服务客户。觅蜂希望把采集、重建、质检、训练、部署和数据回流连成一条链路，但目前披露的产量、数据小时数和覆盖范围仍是企业公布的数据，设备在业务现场的效果以及这些数据对模型能力的提升，还需要后续应用来验证。
The specific change is that robot-model training is beginning to receive a more continuous supply of data closer to real work: JD.com has received a set of data-collection devices about to enter business operations, while Tencent’s Robotics X laboratory and Ant Lingbo have become data-service customers. Mifeng aims to link collection, reconstruction, quality control, training, deployment and data feedback into a single pipeline. However, the disclosed production figures, data hours and coverage remain company-reported figures, and the devices’ performance in business settings—as well as the extent to which this data improves model capabilities—will require validation through subsequent applications.