On August 20, SEER Robotics officially unveiled three strategic initiatives for embodied intelligence. Centered on three directions—advancing a general-purpose robot brain, building real-world data infrastructure, and co-building an evolution network of one million robots—the initiatives further define the company's long-term roadmap for the era of embodied intelligence.

By integrating the robot brain, real-world data, and a large-scale robot network, SEER Robotics aims to build a durable, self-sustaining infrastructure for embodied intelligence—one that allows robot capabilities to be replicated, accumulated, and evolved across an ever-wider range of platforms, tasks, and scenarios, and that ultimately forms an embodied intelligence foundation spanning degrees of freedom, configurations, and modalities.

Advancing a General-Purpose Robot Brain

The fundamental difference between embodied intelligence and conventional AI is that intelligence must ultimately act on the real world through a robot's physical body. A model must not only understand what is happening and what needs to be done; it must also determine how to act—and how to act reliably.

SEER Robotics believes that a robot brain capable of truly entering the physical world requires a complete system running from cognition through execution to real-time feedback. To this end, the robot brain adopts a three-in-one architecture—Model, Cerebellum, and Nerves—that gives robots a complete intelligence system.

• The model layer serves as the cognitive center, responsible for perceiving and reasoning: environmental perception, task understanding, and intelligent decision-making.
• The cerebellum layer serves as the execution center, responsible for turning decisions into action: motion planning, trajectory generation, and real-time execution, ensuring that robot movements are accurate, stable, and safe.
• The nerve layer serves as the connectivity center, responsible for linking brain and body: through unified interfaces and a protocol stack compatible with more than 400 core components, it connects sensors, joints, and actuators, sending control commands downward to joints and actuators while returning position, velocity, torque, and sensor status upward in real time to close the full control loop.

In this way, robot intelligence is no longer confined within the model; it enters the robot's body, carries out tasks in the real world, and adjusts in real time according to the body's state.

The robot brain has long been a core competency underpinning SEER Robotics' long-term growth. As the company enters the embodied intelligence era, it will advance the generalization of the robot brain along three directions: continuously upgrading the brain's hardware carriers, expanding the modular robotics ecosystem, and stepping up investment in embodied models.

• At the hardware level, the robot brain must accommodate an increasingly rich variety of robot configurations and computing requirements, progressing step by step from localized control toward whole-body coordinated control.
• At the ecosystem level, SEER Robotics seeks to connect modules such as robotic arms, joints, dexterous hands, sensors, and actuators, enabling different components to be assembled more quickly into new robot forms.
• At the model level, the company is building sustained capabilities across spatial understanding, task reasoning, motion generation, world modeling, and long-horizon tasks, so that model capabilities can be invoked according to the scenario and task at hand and ultimately delivered into robots through one and the same robot brain.

Across embodied intelligent machines ranging from robotic arms and humanoid robots to robot dogs, SEER Robotics aims to progressively realize the following: bodies may differ, yet one brain connects them all; tasks may differ, yet capabilities remain available on call.

This also marks a decisive step in the robot brain's evolution from traditional control capabilities toward embodied intelligence infrastructure.


Building Real-World Data Infrastructure

What embodied intelligence truly lacks is not data volume, but high-quality data capable of supporting robots in understanding and learning from the physical world. Unlike data drawn purely from the internet, embodied intelligence data must come simultaneously from real robots, real environments, and real tasks, and it must keep accumulating as robots continue to work.

Accordingly, SEER Robotics defines high-quality embodied data along four core dimensions: diversity, authenticity, consistency, and sustainable acquisition.

• Diversity: sufficiently rich data must be generated across different robots, configurations, tasks, and environments.
• Authenticity: data must come from the real physical world, rather than remaining within simulated environments.
• Consistency: data drawn from different robots and scenarios must rest on unified data structures and time systems before it can genuinely feed model training.
• Sustainable acquisition: above all, such data must be produced continuously alongside the long-term operation of robots.

On the basis of this assessment, SEER Robotics further introduces the concept of the complete embodied dataset.

For any given real task, data should not merely record what the robot ultimately accomplished; it should preserve, to the greatest extent possible, the surrounding environment, human demonstrations, real-robot operation, and the processes of failure and recovery. Only when the full course of a task is recorded can a model learn not a single isolated action, but a complete, reproducible, and trainable real-world experience.

To translate this vision into practice, SEER Robotics has formally established an embodied intelligence data subsidiary—Hangzhou Kaiwu Innovation Embodied Intelligence Technology Co., Ltd.—to advance embodied-data business across the board.

This means embodied data is no longer simply a by-product generated naturally as robot products operate; it will become a key foundational capability for SEER Robotics as it builds out its embodied intelligence business.

Around this capability, SEER Robotics is building a complete data infrastructure spanning real-robot collection, multi-source ingestion, data governance, model training, simulation-based evaluation, and capability feedback. It is also building a data and agent platform for embodied intelligence, further connecting the end-to-end chain from real robots to model training and on to capability deployment.

At the same time, SEER Robotics plans to gradually open its embodied intelligence datasets and in-house evaluation benchmarks starting in the fourth quarter of this year, driving real-world data and robot capability evaluation beyond a scattered, fragmented state toward more systematic infrastructure development.

Ultimately, data generated as robots perform real work will enter the training pipeline, produce new model capabilities, and return to the robots through OTA updates: real-world operation → data accumulation → model iteration → capability upgrade → renewed operation.

The more robots work, the richer the data they generate; the richer the data, the better models understand the real world; and the more capable the models become, the more numerous and complex the tasks robots can accomplish.
In this way, data is no longer the raw material for one-off training; it becomes the core engine that connects robots' real-world operation with the continuous evolution of intelligence.


Co-Building an Evolution Network of One Million Robots

The scale and value of data ultimately come from robots that keep operating in the real world.

Accordingly, SEER Robotics is launching the "Ladder Program": by 2030, together with its ecosystem partners, it aims to bring one million embodied intelligent robots into deployment across a wide range of body forms, capable of continuously performing diverse operational tasks in all kinds of real-world scenarios. The company formally announced the program's launch and released the first cohort of 20 ecosystem partner companies.

One million robots is far more than a volume target.

The more industries robots enter, the richer the tasks they perform, and the more diverse their body forms and modes of perception, the more physical-world experience they can accumulate. Once this experience enters the data and model pipeline, it generates new robot capabilities, which in turn drive more robots into real-world scenarios.

Thus, large-scale robot deployment is no longer merely a commercial outcome; it becomes the infrastructure on which embodied intelligence continuously evolves.

For such a robot network to truly operate, robots must be equipped with cloud connectivity, always-on real-time access, data backflow, and OTA upgrade capabilities, enabling them to continuously collect real-world data and acquire new capabilities throughout long-term operation.

This is precisely where the Ladder Program differs from a simple scale-up of robot deployment.

Robot bodies, core components, models, data, computing power, and real-world scenarios are distributed among different industry partners. Taking the robot brain as the connecting point, SEER Robotics aims to bring more robots genuinely into the physical world and to enable partners across industries to jointly build this evolution network.

The scale of robots determines the breadth of real-world experience; the accumulation of real-world experience in turn determines the speed at which embodied intelligence evolves.

This is the true significance of one million robots: what the network connects is not a simple headcount of machines, but a robot evolution network spanning different bodies, tasks, and environments—one capable of continuously generating real-world experience.


Three Strategies Converge,Toward an Embodied Intelligence Foundation

If embodied intelligence is to be a technology that genuinely enters the physical world, stronger models alone are far from enough. Models must enter robots; robots must enter real scenarios; and real scenarios must continuously feed back into the models.

This is why SEER Robotics thinks about the robot brain, real-world data, and a large-scale robot network within one unified strategy.

The robot brain brings intelligence into robots; real-world data enables robots to keep evolving; and one million robots give this evolutionary system the real-world scale it needs.

Only when the three are genuinely connected does embodied intelligence begin to take shape as an infrastructure that can be continuously replicated and evolved.

On this foundation, SEER Robotics will go further to build an embodied intelligence foundation spanning degrees of freedom, configurations, and modalities.

• Cross-degrees-of-freedom means the ability to work across different joint counts and motion capabilities.
• Cross-configuration means coverage of different body structures, including humanoids, wheeled robots, robotic arms, and robot dogs.
• Cross-modal means the ability to process different perceptual inputs, including vision, force sensing, tactile feedback, and joint states.

Ultimately, what different robots share will no longer be merely software interfaces, but a growing body of reusable foundational intelligence capabilities.

And once these underlying capabilities are truly in place, the way people interact with embodied intelligence will change as well.

In the past, a new robot task usually meant starting data collection, algorithm development, model training, and on-site deployment all over again. In the future, these complex processes can be progressively absorbed into the backend: users need only tell the robot what to accomplish, while the system judges whether existing capabilities are up to the task—and, when they are not, automatically drives the required data collection, training, evaluation, and OTA updates.

Truly mature embodied intelligence does not require every user to become a machine learning expert; it gives robots the ability to keep acquiring new capabilities.

In this light, what SEER Robotics is building today is not merely a more powerful robot brain, a data platform, or a robot network, but an infrastructure that connects intelligence, robots, and the real world.

As increasingly complex capabilities are consolidated behind the system, what users ultimately see will no longer be models, data, or algorithms, but a robot that understands requirements, executes tasks, and keeps gaining new capabilities.

Models and data ultimately recede behind the scenes; what remains are capabilities that robots can put to work directly.

This is the ultimate goal of SEER Robotics' embodied intelligence strategy: to remove the barriers to embodied intelligence. And it points onward to the mission to which SEER Robotics has long been committed: Drive a more open, diverse era of intelligence, making AI robots accessible to all.