记者昨天从国网天津电力获悉，日前，该公司电能计量器具检定、电缆隧道巡检作业两处电力领域具身智能实景实训场地对外开放，将真实电力作业场景搬进训练区，打造面向产业链开放共享的“机器人学校”，为机器人真机训练、算法迭代、作业验证提供高保真的实训环境。
Reporters learned yesterday from State Grid Tianjin Electric Power that the company's two electric power embodied intelligence real-scenario training grounds—for electric energy metering instrument verification and cable tunnel inspection operations—have recently opened to the public, bringing real power operation scenarios into the training area and building an industry-chain open and shared "robot school," providing high-fidelity training environments for robot real-machine training, algorithm iteration, and operation validation.
据了解，国网天津电力从10余个场景中遴选2个典型场景作为实景实训场地对外开放。实训场地坚持“最小干预、利旧复用”原则，全部依托现有生产作业现场改造，最大限度贴合实际业务需求。其中，电能计量器具检定实训场地设在营服中心表计检定车间，配备表计检定台、周转箱及多品类表计设备，可开展机器人挂摘表计、封印核查、表计搬运等双臂精细操作实训。
It is understood that State Grid Tianjin Electric Power selected 2 typical scenarios from over 10 as real-scenario training grounds open to the public. The training grounds adhere to the principle of "minimal intervention, reuse of existing facilities," all renovated from existing production operation sites to fit actual business needs to the greatest extent. Among them, the electric energy metering instrument verification training ground is located in the meter verification workshop of the Marketing Service Center, equipped with meter verification benches, turnover boxes, and multi-category meter equipment, capable of conducting dual-arm fine manipulation training such as robot meter hanging and removal, seal verification, and meter handling.
电缆隧道巡检作业实训场地设在电缆公司220千伏绿水园电缆隧道，场地全长约3公里，还原设备密集、极窄狭长、密闭潮湿、卫星拒止等地下隧道工况，支撑机器人开展自主导航、极窄通行、长距离续航、设备巡检、火情应急处置等实景训练。目前，实训场地已面向国内科研院所、高校、机器人整机企业、算法研发单位等开放共享，并联合中国电科院、北京人形机器人创新中心、云深处科技、山东理工大学等单位开展场景研发。
The cable tunnel inspection operation training ground is located in the Cable Company's 220kV Lvyuan cable tunnel, with a total length of about 3 kilometers, recreating underground tunnel conditions including dense equipment, extremely narrow and long passages, sealed humidity, and satellite denial, supporting robots in real-scenario training for autonomous navigation, extremely narrow passage, long-distance endurance, equipment inspection, and fire emergency response. Currently, the training grounds are open and shared with domestic research institutes, universities, robot complete machine enterprises, and algorithm R&D units, and have partnered with the China Electric Power Research Institute, Beijing Humanoid Robot Innovation Center, Deep Robotics, Shandong University of Technology, and others for scenario R&D.
这两处实训场地的选址逻辑值得细看。电力行业的作业场景有几个鲜明特点。一是标准化程度高，表计检定、隧道巡检这些任务是固定的、重复的，适合机器人学习。二是环境具有挑战性，电缆隧道里设备密集、空间狭窄、潮湿、没有卫星信号，这些条件对机器人的感知、导航、续航都是硬考验。三是安全需求明确，电力作业本身有危险，让机器人替代人工进入危险环境，客户付费的意愿是真实的。这三点加在一起，让电力场景成为具身智能落地的优质试验场。
The site selection logic of these two training grounds deserves closer examination. The power industry's operation scenarios have several distinctive features. First, high standardization—meter verification and tunnel inspection are fixed, repetitive tasks suitable for robot learning. Second, challenging environments—cable tunnels have dense equipment, narrow spaces, humidity, and no satellite signals, conditions that are hard tests for robot perception, navigation, and endurance. Third, clear safety needs—power operations are inherently dangerous, and letting robots replace humans in hazardous environments means customers' willingness to pay is real. Together, these three make power scenarios a high-quality testing ground for embodied intelligence deployment.
国网天津电力的做法，和前面讨论的北京人形数据与训练基地、上海推动8000台人形机器人进工厂，是同一个趋势的不同表现。地方政府和央企正在把真实场景开放出来，作为机器人训练和验证的基础设施。这种做法解决了具身智能行业的一个核心矛盾：实验室和样板间里训练出来的机器人，到了真实场景里往往表现不佳。真实场景的数据、真实场景的干扰、真实场景的边界条件，只有在真实场景里才能采集到。国网天津电力把两处现场改造成实训场地，等于给机器人企业提供了一个低成本、高保真的真实场景入口。
State Grid Tianjin Electric Power's approach, alongside the previously discussed Beijing Humanoid Data and Training Base and Shanghai's push for 8,000 humanoid robots into factories, is a different manifestation of the same trend. Local governments and central enterprises are opening real scenarios as infrastructure for robot training and validation. This approach solves a core contradiction in the embodied intelligence industry: robots trained in laboratories and model rooms often perform poorly in real scenarios. Real-scenario data, real-scenario interference, and real-scenario boundary conditions can only be collected in real scenarios. State Grid Tianjin Electric Power converting two sites into training grounds is effectively providing robot companies with a low-cost, high-fidelity real-scenario entry point.
