Robots are shifting from "mass production" to "mass sales", and Qizhi Openmind is committed to filling the gap in the middle layer of industrial embodied intelligence.
Robots seem to be one step closer to the real production environment.
As a platform that centrally demonstrates robot capabilities, at this year's World Robot Conference, more and more enterprises have directly built specific scenarios such as factories, allowing robots to complete tasks including sorting, assembly and inspection on site. This conference also set up a Procurement Day for the first time, where central SOEs bring actual scenarios and procurement demands to find suppliers, which releases a clear signal: the industry's focus has shifted to whether robots can continuously, stably and economically create value.
However, booth demonstrations are not completely equivalent to production line implementation. Between completing a task in a relatively controllable environment and continuously coping with workpiece deviation, environmental changes and equipment abnormalities at real industrial sites, there are still multiple thresholds such as data, process, reliability and engineering delivery.
To bridge this gap, Qizhi Openmind also brought its own solution at the conference. This enterprise, spun off from the listed intelligent robot company Efort, released two new products: the new HALO Skill Suit is responsible for converting people's real operation experience into trainable data, while the composite robot verifies how the same set of capabilities can complete the closed loop from perception, planning to execution at industrial sites.
Different from most enterprises that focus on the operation capabilities of robots in factory scenarios, Qizhi Openmind tries to solve the problem of how embodied intelligence can further complete training, deployment and reuse.
From Rule-Driven to Data-Driven
In fact, industrial embodied intelligence has been accompanied by doubts from the very beginning. After all, industrial robots are no longer new things on manufacturing production lines in industries such as automotive, 3C and lithium battery. After decades of development, robotic arms, combined with transmission equipment, tooling fixtures and machine vision, have been able to continuously complete a series of tasks including welding, handling, spraying and assembly.
However, what industrial embodied intelligence really tries to enter is the "remaining part" that has not been economically covered by the automation system.
A relevant person in charge of Qizhi Openmind told 36Kr that traditional industrial robots are driven by rigid rules. As long as the workpiece, position and environment remain unchanged, the robot can perform tasks repeatedly. But this rule-driven model cannot cope with flexible operation scenarios. Shifting from rule-driven to data-driven to improve the generalization ability of industrial robots has become the core value of embodied intelligence entering industrial scenarios.
However, the person in charge also admitted that there may be a long-standing "impossible triangle" for industrial embodied intelligence, that is, it cannot meet the requirements of fast response speed, high execution accuracy and strong cross-scenario generalization ability at the same time. This is because the more scenarios the model adapts to, the more complex the reasoning process is usually, and the more difficult it is to balance response speed and execution stability.
Therefore, the more realistic path for the implementation of industrial embodied intelligence in the short term is to first constrain the problem in a relatively closed scope, and train vertical capabilities for a certain type of process. By moderately narrowing the generalization boundary, we can obtain faster response speed and more reliable execution results, first meet the needs of industrial scenarios for robot operation stability, and then gradually expand the capability scope of robots.
Further decomposition will show that the construction of vertical capabilities of industrial embodied intelligence still faces two practical thresholds.
The first one is data. With the limitations of laboratory teleoperation and simulation data emerging, the value of data from the real production process has become increasingly prominent. However, such data is mainly held by end customers or system integrators with industry experience, and often involves production processes, equipment parameters and trade secrets, which are difficult to open directly to the outside world, eventually aggravating the shortage of effective data required for training.
The second one is process knowledge. The aforementioned person in charge of Qizhi Openmind mentioned that a project team once repeatedly supplemented data and retrained the model after the robot had abnormal movements, but the problem was still not solved. Finally, with the reminder of the personnel who knew the process, they found that the root cause was only that the ground wire was not connected. In other words, robot manufacturers may be familiar with the ontology, motion control and models, but they do not necessarily understand every specific process. Therefore, the implementation of industrial embodied intelligence also requires the joint participation of process and on-site engineering experience.
It can be seen that for industrial embodied intelligence to truly move towards implementation, what is missing is not just a stronger model, but also a complete toolchain that can collect data from real operations, convert process experience into robot skills, and further deploy them to real machines for execution. This is exactly the intermediate layer that Qizhi Openmind tries to make up for.
Building a Platform Layer Between Ontology and Scenarios
Following the above underlying logic, the two products released by Qizhi Openmind this time are located at the two ends of the formation of industrial embodied intelligence capabilities: the HALO Skill Suit solves the problem of how real data is collected, while the composite robot verifies how these data and model capabilities can be deployed to real machines to complete tasks in specific industrial scenarios.
According to Qizhi Openmind, the HALO Skill Suit can synchronously collect multi-modal data including 8K panoramic images, first-person perspective RGBD information, hand haptics, 27-node inertial motion capture and surface electromyography. Compared with simply collecting videos, these data of different modalities can more completely restore the movement process of people in real operations, as well as the force and physical state used when completing movements, providing training materials closer to the physical world for robots to learn fine operations.
In terms of engineering improvement, the new generation HALO Skill Suit features an "integrated wearing" design, which deeply integrates the whole-body sensing unit into the clothing ontology without exposed wiring. It not only supports quick putting on and taking off, fits stably and is easy to clean and maintain, but also truly realizes "collecting as soon as you put it on". While greatly improving the wearing experience and the consistency of multiple batches of data, it also creates conditions for the large-scale collection of real industrial data.
Since the collection process is not bound to a certain robot, these data can also be used for model training and skill development of robots of different brands and different configurations.
However, collecting data is only the first step. Human movement data still needs to be cleaned, processed and trained to be converted into skills that robots can understand and execute, and then adapted to specific ontologies and industrial scenarios.
To open up the above links, Qizhi Openmind has built a complete general technical base for intelligent robots. The HALO Skill Suit is responsible for collecting real-world data, HumanGPT provides model capabilities, the Dayan Data Platform undertakes data management and processing, the Ink Ruler IDE is used for skill development and debugging, and Openmind OS connects upper-layer applications with different robot ontologies, thus covering the complete process from data collection, model training, skill development to real machine deployment. On this open base similar to "Robot Android", developers can develop robot skills for specific processes, and then adapt them to robot ontologies of different brands and different configurations.
The composite robot provides an execution-side verification carrier for this toolchain. It combines a robotic arm, 3D vision and a mobile chassis. The mobile chassis moves between different stations, the vision system identifies the workpiece and its position, and the robotic arm then completes grasping, sorting and assembly. Compared with robotic arms that can only repeatedly execute predetermined trajectories at fixed stations, composite robots need to continuously deal with changes in their own position, operation objects and surrounding environments, so as to adapt to more flexible production tasks.
At present, this composite robot has undergone long-term verification on the real production line of a main engine factory, and is used for integrated operations of high-density sorting and precision assembly.
"In terms of product form, the composite robot is not a underlying innovation. What we show more is its openness from the development to deployment process." The person in charge of Qizhi Openmind told 36Kr.
This sentence also clarifies Qizhi Openmind's positioning of itself in the industrial embodied intelligence industry chain: As a tool platform, it encapsulates underlying capabilities such as data collection, robot control and task orchestration into the development environment, so that partners who master process knowledge can develop vertical applications according to actual needs. "Our idea is that everyone can develop robots, and everyone can build their own robots for vertical scenarios."
Division of Labor May Be More Important Than Full-Stack Self-Development
Qizhi Openmind's choice to position itself as a tool platform is inseparable from Efort's long-term accumulation in the field of industrial robots.
The predecessor of Qizhi Openmind was an intelligent project team established inside Efort in 2017, and it was not officially established as a company until 2024. Therefore, this is not a startup team that enters the industrial embodied intelligence field from scratch. It has financial support brought by Efort as a mature company, as well as intelligent robot projects from Efort, and technical accumulation precipitated by scientific research projects such as national key R&D programs and major special projects.
In addition to financial and technical support, Efort also provides industrial sites for capability verification. At present, Efort produces about 20,000 robots every year, with a cumulative shipment of more than 100,000 units, and it is also connected with end customers and more than 400 system integrators. How these robots enter factories, how to complete secondary development around specific processes, and how to generate actual value in the end, undoubtedly constitute the starting point for Qizhi Openmind to observe the industrial automation industry chain.
Such historical evolution has also shaped Qizhi Openmind's judgment on the industrial division of labor of industrial embodied intelligence. In the interview, the relevant person in charge of Qizhi Openmind frankly told 36Kr, "The full-stack self-development emphasized in the current embodied intelligence field is essentially a manifestation of unclear industry division of labor." Algorithm teams do not necessarily understand on-site engineering problems, robot manufacturers can hardly master the processes of every industry, and it is also costly for system integrators who understand processes to make up for model, motion control and data processing capabilities from scratch.
In the long-term development process, traditional industrial robots have formed a professional division of labor among core component suppliers, complete machine manufacturers, system integrators and end customers. Industrial embodied intelligence also needs to form a new collaborative relationship: robot enterprises provide ontology and execution capabilities, platform enterprises provide data, models, operating systems and development tools, system integrators use process knowledge to develop vertical applications, and end customers provide real demands, standard operation procedures and continuous operation data.
According to the plan, Qizhi Openmind will start with the switching and adaptation of Efort's underlying robot system, and promote more robot manufacturers to enter the platform ecosystem. The ultimate goal is to realize independent control in the industrial embodied era through a self-developed industrial embodied intelligence development base that can adapt to robots of different brands and configurations, so that developers do not have to be bound by a single robot brand or a single software and hardware architecture.
However, it also needs to be seen that from completing verification inside the Efort system to truly becoming an industry-wide platform, Qizhi Openmind still needs to prove that its base can gain the trust of other robot manufacturers, system integrators and end customers, and form a sufficiently rich vertical application ecosystem.
This World Robot Conference coincided with the listing of Unitree, "the first stock of humanoid robots", on the Sci-Tech Innovation Board. Its market performance in the initial stage of listing undoubtedly reflects that the capital market still has high expectations for the embodied intelligence and robot industry. However, compared with the optimistic long-term imagination of the capital market, the real entry of robots into the production environment still needs to be promoted one by one from specific links such as data, models, ontology and engineering delivery.
As the person in charge of Qizhi Openmind said: The mass production of robot ontology is only the first step of industrial development. Whether more developers who understand scenarios and processes can participate and form sufficiently rich vertical applications that can create actual value determines whether robots can further move from mass production to mass sales.