At the 2026 World Robot Conference forum, held in Beijing from August 19 to 22, Sudu Technology co-founder and CEO Han Zheng stated that if a model framework cannot achieve over 99.9% reliability—meaning a 99.9% success rate on a single attempt or after two error-correction attempts—it will be very difficult to truly commercialize. He emphasized that while the industry can continuously experiment and make mistakes when exploring foundational frameworks, moving toward commercialization requires a fundamental rethink of what kind of embodied AI solutions are actually needed in real business scenarios.

Looking back at the robotics industry, traditional industrial robots rely on manual pre-programming or limited object generalization capabilities enabled by industrial vision. They must guarantee over 99% reliability before they can be deployed in most scenarios. For navigation-type tasks, such as autonomous driving or factory AGV carts, the standards for precision and success rates are even higher, as tolerance for errors is extremely low.

He added that in the next phase, as robots enter the embodied AI stage, regardless of the technical route chosen, the first question beyond technical exploration is whether 99.9% reliability can be achieved before the technology can be deployed on production lines. He noted that some peers in specific fields have managed to push success rates close to 99% by using massive amounts of data, imposing constraints on objects and environments, and supplementing with real-machine data collection, manual data collection, and real-machine reinforcement learning. However, this approach is difficult to scale and replicate.

If every new deployment scenario requires large teams of bachelor's, master's, and doctoral-level personnel to perform heavy data collection, repeatedly fine-tune training, and even risk overfitting the data, then the advantages of embodied AI over traditional industrial robots in terms of object, environment, and task generalization will no longer exist.