From material handling and line-side delivery in factories, to delivery services in commercial settings, and to inspection in campuses and industrial parks, as well as robotics development and hands-on practice in educational environments, robots are entering an ever-wider range of industries and applications.

As deployment expands, robot form factors diversify and the tasks they undertake grow more complex. From a single robot addressing one specific problem to multiple robots each taking on different tasks in parallel, both the scale and complexity of robotic deployments are increasing.

As robots enter more environments, the questions enterprises and developers must consider also evolve: How do you choose the right robot for each scenario? Once deployed in real-world settings, can robots perform tasks reliably? And when multiple robots operate simultaneously, how can they be effectively managed?

The next step in robotics is not simply adding more robots. What enterprises truly need is the ability to develop, select, and deploy the right robots more efficiently across diverse scenarios and tasks—and to make them perform reliably in real business operations.

Different scenarios impose different requirements on robots. Industrial settings demand robots capable of material transport, precise fork pickup, and stacking; commercial services may involve item delivery and mobile service tasks; and educational environments require robots that support development, debugging, and hands-on application practice.

Different tasks call for different robot form factors, mobility capabilities, and execution methods.

For developers, lowering the barrier to developing and adapting robots across different configurations is critical to getting solutions off the ground. For end users, choosing the right robot based on actual requirements is equally decisive for deployment outcomes.


This is precisely where adaptability comes in: a shared set of foundational capabilities—the robot brain—that enables the development and deployment of diverse robot types.

The SEER Robotics Nebula Platform already supports over 2,000 robot SKUs. At CeMAT ASIA 2026, we will demonstrate on-site how multiple robot types, all powered by the same robot brain, execute different tasks: dexterous grasping and manipulation by a humanoid robot, collaborative navigation of AI delivery robots in extremely narrow passageways, and omnidirectional pickup and drop-off by smart forklifts.

A robot's capabilities cannot be judged by a single demo; they must be proven through real-world tasks.


The SEER Robotics robot brain has already been deployed across industrial, commercial, and educational service sectors, accumulating real-world operational data through live applications to drive continuous evolution of the robot brain.

To date, more than 50,000 robots powered by the SEER robot brain are operating in real-world settings, spanning different hardware platforms, tasks, and environments. This large-scale real deployment enables SEER to continually encounter richer scenarios and challenges, and—under customer authorization and in compliance with data governance requirements—to accumulate real-world operational data usable for training and optimization.

By building a data flywheel of "deployment—data feedback—closed-loop training—model iteration—expanded deployment," SEER Robotics uses data gathered from real-world scenarios to train and optimize the robot brain. Iterated models are then redeployed to robots and continuously validated and improved through new real-world tasks, driving steady advancement in robots' ability to execute complex operations.

From adapting to diverse robot form factors to driving capability iteration through real-world scenarios, SEER Robotics is enabling the robot brain to support an ever-growing range of deployed robotic applications, continuously pushing the boundaries of what is possible.

November 3–6: We invite you to visit us at CeMAT ASIA 2026, Booth W2-G4-1, Shanghai New International Expo Centre. We look forward to welcoming you.