Yipai Technology's Xu Yuhan: Embodied Intelligence Entering Factories Must First Overcome Three Hurdles of Data, Reliability, and Economic Viability

 Gasgoo Munich- "Even if embodied intelligence achieves 99% accuracy, it remains intolerable for a factory. For software, 99% might be a false positive; for a production line, it is a shutdown." On July 3, at the 2026 Embodied Intelligence Industry Scenario Integration Conference hosted by Gasgoo, Xu Yuhan, AI product director at Yipai Technology, pointed out the practical dilemmas facing the industrial deployment of embodied intelligence.

Xu divides smart manufacturing into two halves. The first half is software-defined manufacturing intelligence, where AI serves as a tool to augment human capabilities, using agents to boost problem detection rates and solution efficiency. The second half is a hardware revolution driven by embodied intelligence—shifting from human-operated machinery to autonomous operation, enabling large-scale substitution of human labor.

Yipai Technology has historically focused on multi-agent collaborative AI platforms. Spanning Quality Guardian, Equipment Guardian, and Efficiency Guardian, these systems collect IoT data, process metrics, and quality inspection images to perform root cause diagnosis and execute feedback controls.

Yet, as Yipai explores the implementation of embodied intelligence, Xu has identified several unavoidable obstacles. The first is a data shortage. "The industrial sector simply cannot provide the volume of data required for embodied intelligence," he noted. Furthermore, simulation data falls short in real-world production environments, making it impossible to directly deploy trajectories and strategies simulated on the line to actual hardware.

The second hurdle is the lack of a specialized "brain" for industrial assembly. In final assembly workshops, tasks like wire harness clipping and bonding—stations where traditional automation fails—still stump embodied intelligence. It cannot yet precisely plan how to unravel wire harnesses or execute precision connections.

Third, high-precision scenarios remain reliant on traditional 2D and 3D vision positioning. "The deployment process still involves tedious intelligent debugging work, failing to deliver the high generalization expected from embodied intelligence."

Fourth is reliability—reiterating that "99% accuracy on a production line means a shutdown." Fifth is economic feasibility. In terms of ROI, embodied intelligence still struggles to compete with traditional automation. At similar price points, it fails to demonstrate advantages in efficiency, cycle time, or stability.

He urged the industry to focus on two fronts. First, solving high-flexibility scenarios that traditional automation cannot handle—such as wire harness organization, precision clipping, and connector insertion. "Only by mastering these can embodied intelligence offer a unique competitive edge," he said. Second, establishing a new paradigm for data collection. This could involve frontline workers in data generation through teleoperation, motion capture, and VR devices, driving qualitative improvements through sheer volume.