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The challenge of retaining young employees in repetitive, monotonous, and high-stress assembly line jobs is prompting factories to increasingly collaborate with robotics companies to introduce androids. A senior executive from Longcheer Technology stated that last week, they hosted a live-streamed public stress test showcasing robots working on one of their production lines.
“Young workers typically stay at factories for about three months—sometimes up to six months,” explained Zhang Long, who manages Longcheer’s pilot program with humanoid robotics startup AgiBot at their Nanchang facility. “By year’s end, most positions may experience complete employee turnover.”
Based in Shanghai, Longcheer is a contract manufacturer specializing in smart electronic devices. They partnered with AgiBot to deploy eight Genie G2 androids for tablet inspection on a production line, with their performance broadcast live over six days last week. During that time, the robots inspected over 17,000 tablets with a success rate of 99.99%.
Implementing humanoid robots may enable factories to cut operational costs, boost productivity, and fundamentally change their cost models, noted Ai Wen, project lead for AgiBot’s Genie division. He added that a Genie G2 costs roughly the same as the two-year wages of an assembly line worker. As manufacturing costs for robots decrease, their economic benefits are expected to become even more significant.
During an onsite visit, the eight Genie G2 units operated cohesively at the Nanchang site. As they executed their tasks, they also communicated with testing equipment to monitor product testing continuously. Based on test outcomes, they adjusted their operations in real time, ensuring smooth workflows.
Zhang highlighted that the robots’ efficiency and first-pass success rate already meet the factory’s standards.
Future Outlook
While robotic arms currently used for quality control in the factory’s phone assembly line outperform the Genie G2 in certain tasks, adding AI capabilities to robotic arms in a smart manufacturing environment presents considerable challenges—particularly in data collection, model training, system integration, and high upgrade costs, Zhang pointed out.
However, since smart manufacturing demands equipment capable of end-to-end data connectivity, flexible deployment, autonomous decision-making, and continuous self-optimization, the Genie G2 demonstrates much greater potential for future advancements.
Once integrated with factory data networks, robots at their workstations can connect with machinery, inspection tools, and human operators, forming a unified operational system, an employee involved in robot deployment told this publication. This integration allows for real-time data analysis, product status monitoring, and timely alerts, aiding engineers in troubleshooting and swift decision-making.
Remaining Challenges
That said, numerous technical hurdles still need addressing. An industry source explained that before robots can fully assume roles in factories, they must undergo multiple forms of intelligent training. They need to meet specific capability benchmarks and pass product-specific testing phases.
“Developers must submit detailed data—such as gripping force control, operational stability, and other product-related metrics—to the client,” the source stated. “Only after client approval can the robot officially be deployed,” emphasizing that the verification process can be lengthy.
Scaling robot deployment across production lines also depends heavily on improving overall success rates. For manufacturing of electronics, communications devices, and consumer products, the success rate must surpass 99.5 percent, according to AgiBot’s AI chief.
Even if a robot performs a single task flawlessly 100 percent of the time, it doesn’t meet deployment criteria if it struggles with more complex tasks, he added. Current embodied intelligence models lack the sophistication and generalization capabilities needed, which limits the pace at which robots are integrated into factories, a source from an investment firm noted.
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