Interview with Axis Robotics Founder: Robot Data Becomes the New Darling of Capital, How Do 'Shovel Sellers' Strike Gold?

Author: Zen, PANews

In the past year, robot/Physical AI data has rapidly transformed from a relatively obscure infrastructure segment into a fiercely competitive new arena, with capital pouring in at an astonishing rate into this field known as embodied intelligence, or 'shovel sellers'.

This trend is particularly evident in China. In the first half of 2026 alone, 25 Chinese embodied intelligence data startups collectively secured over 17 billion yuan in funding. Among them, Guanglun Intelligent has completed multiple rounds of large financing this year; the data company Mifeng Technology, incubated by Zhiyuan Robotics, also quickly raised hundreds of millions shortly after its establishment.

In overseas markets, a number of startups focused on robot training data have begun to emerge. XDOF announced in June this year that it had completed $70 million in funding; by the end of July, Axis Robotics completed a $12 million seed round led by Hack VC.

Axis positions itself as a "Physical AI data engine," currently focusing on distributed contributor networks and web-based simulation data collection as key entry points, while continuing to expand into first-person perspectives, model post-training, and other data forms. It has already collected over 2.2 million robot trajectory data points through browser remote operation, accumulating a total data duration of 28,000 hours.

However, despite the ample capital influx, robot data remains a rapidly changing and far from settled market. Key questions are still in the exploratory stage, from how large the data demand will ultimately reach, to how to choose between different paths such as real machines, simulation, and first-person perspectives, and whether data companies can form sustainable business models.

In light of these issues, as well as Axis's own data production model, technical route, and future development plans, PANews engaged in a dialogue with Chris Feng, the founder of Axis Robotics.

The Data Gap in the Age of Physical AI

Chris did not start from the robot body or algorithms, but rather entered the robot data arena through consulting, investment, and data infrastructure.

What truly prompted him to enter the robotics field was the massive data gap that emerged in the age of Physical AI.

"Taking GPT-3 as a reference, its training requires about 15 million hours of human internet data," Chris stated. The physical world that robots need to process is far more complex than the digital world, requiring not only recognition and logical abilities but also an understanding of space, actions, object states, and what changes will occur after actions are taken.

Therefore, if the robotics field is to have its own "GPT moment," the data required could reach 100 million hours or even higher. Besides the obvious differences in data scale, the foundational data for both fields is completely incomparable.

Before the emergence of large language models, the internet had developed for decades. Various web pages, books, forums, code repositories, and a wealth of digital content formed a natural data resource through long-term accumulation.

In contrast, human behavior in the real world has not undergone a similar systematic recording process. People cook, organize items, and operate tools every day, but these actions are rarely recorded continuously, let alone transformed on a large scale into standardized data that can be directly used for robot training.

With the development of technologies like VLA models and world models, robotics model companies have begun to become the most direct demand side for such data, while some robot body companies are also simultaneously building model and data capabilities. Meanwhile, the forms of robots themselves are rapidly diversifying, from humanoid robots and robotic arms to specialized devices designed for industrial and service scenarios, further generating differentiated data demands.

As the demand scale continues to expand, traditional data production methods quickly reveal their limitations.

Early robotics companies typically relied on real machine remote operation, where humans directly controlled robots to complete tasks and recorded action trajectories. This type of data is closest to real robots but is slow to produce, costly, and limited by the number of robots, venues, and operators.

More importantly, the real world is highly complex and diverse. Relying solely on a limited number of robots and collection scenarios within a company makes it difficult to cover the environments that models may encounter in the future. If the foundational models for robots ultimately require tens of millions of hours of data or more, simply increasing the number of robots, venues, and remote operators will not achieve economically effective linear expansion.

Thus, how to continuously produce sufficiently diverse data that can genuinely improve model performance at controllable costs and in a scalable manner has become one of the core issues facing the Physical AI data industry, as well as a commercial opportunity that independent robot data companies are competing for.

The "Pyramid" of Robot Data: Balancing Accuracy, Scale, and Generalization

Another complex issue with robot data is that data from different sources exhibits significant differences in authenticity, cost, scalability, and cross-body reuse capability.

Chris roughly categorizes the main routes into real machine remote operation, simulation, and human first-person perspective data. If we look at the direct matching degree between data and robot bodies, a "pyramid" can be formed.

At the top of the pyramid is real machine remote operation.

Operators directly control real robots to complete tasks such as grasping, placing, and moving, while sensors simultaneously record the robot's joints, end effectors, visual and action signals. The greatest advantage of this type of data is that the training data itself comes from real robots, thus providing the most complete physical information with minimal differences from the target body.

However, the cost and scale issues of real machine data are equally prominent. According to industry data, the comprehensive cost of one hour of high-quality real machine remote operation data can reach $200. More importantly, this type of data is usually highly bound to specific robot bodies, limiting its cross-body reuse capability. For example, action trajectories collected on the Unitree G1 cannot be directly adapted to robots of different forms.

In the middle of the pyramid is simulation data.

Robot simulation attempts to replicate the size, joints, action range, and task environment of real robots as closely as possible in a virtual space, allowing operators or programs to control virtual robots to complete tasks. Common simulation platforms used in robot research and development include Isaac Sim, MuJoCo, and others.

Compared to the real world, the outstanding advantages of simulation are scalability and controllability. It is impractical to build thousands of kitchens, warehouses, or factories to collect data in the real world, but in a virtual environment, similar scenes can be quickly replicated and run in parallel. Existing trajectories can also be expanded to more different environments by changing parameters such as lighting, materials, camera positions, object types, and spatial layouts.

At the same time, the task states in the simulation environment are also easier to read accurately. For example, whether a task is completed, whether a robotic arm has collided, and what position an object ultimately occupies can all be directly obtained from the environment, making data validation and automatic filtering easier.

However, the limitations of simulation data are also clear: virtual environments always struggle to fully replicate the real world. Friction, object deformation, contact states, sensor noise, and numerous uncontrollable factors can lead to models performing well in simulation but experiencing performance drops when transferred to real robots. How to narrow this "simulation-to-reality" gap has been a core issue in robotics research.

Another route that has seen significant growth in the past two years is human first-person perspective data, which sits at the bottom of the pyramid.

This method attempts to reduce reliance on expensive robot bodies by having data contributors wear head cameras, GoPros, or directly use smartphones to record real behaviors such as cooking, folding clothes, organizing rooms, and operating tools.

The basic logic is that if the visual and action logic of future robots is highly similar to that of humans, then recording on a large scale "how humans interact with the world" could theoretically provide extremely rich data for the foundational models of robots.

The most attractive aspect of first-person perspective data is its potential scale and environmental diversity. Compared to deploying a large number of robots for remote operation, having ordinary people record behaviors in real scenarios such as homes, factories, and shopping malls has lower collection costs and participation thresholds, making it easier to cover a wide variety of environments.

However, Chris also points out that human first-person data has a significant uncertainty: its training value largely depends on the future body form of the robots.

If robots adopt non-humanoid designs such as wheeled chassis, robotic arms, or specialized grippers, the direct transfer value of human operation data at the action level will significantly decrease. Even if humanoid robots become mainstream in the future, human actions cannot be directly replicated by robots; hand and body movements still need to undergo action redirection to be converted into trajectories that robotic joints and hands can execute.

Another often-overlooked issue is the visual perspective. Ideally, first-person data should be as close as possible to human eye level, but different collection devices may be mounted on the head, chest, or other locations. Changes in perspective can significantly alter the spatial relationships between hands, objects, and cameras, posing a considerable challenge for robot models that need to learn three-dimensional space and operational relationships.

Therefore, in Chris's view, real machine, simulation, and first-person data do not have a simple substitute relationship. Each addresses different issues: real machines ensure accuracy, simulation is responsible for scaling, and human videos provide diversity in real-world scenes. The future robot data system is more likely to be a combination and complementarity of different data sources.

Additionally, recent research has begun to downplay the single pursuit of "optimal data." Dyna Robotics' latest release, Dyna-2, has observed scale effects from human behavior data to robot capabilities by relying solely on over 1 million hours of human first-person video for pre-training. Axis's research also indicates that filtered and processed simulation trajectories can continuously improve model performance as data scale expands.

Becoming Data Producers: Building a Physical AI Data Engine

Compared to merely pursuing a high degree of matching between individual data points and target bodies, scale, diversity, and the ability to truly translate into model capabilities are becoming important metrics. Therefore, it is challenging for robot data companies to meet the continuously changing demands of models relying solely on a single collection method. This also points to a more core question: should data companies merely complete collection and processing according to client requirements, or should they engage more deeply in the design and production of data needed for model training?

Axis has chosen the latter and positions itself as a "Physical AI data engine." In Chris's view, "data labeling" and "data production" are not the same level of business.

Data annotation deals with existing data, where clients decide what and how to label, while service providers are responsible for executing according to the rules. Data production, however, requires further involvement; data companies must not only collect and process data but also participate in determining what data the model lacks, design tasks around training objectives, organize collection, and adjust the next round of data based on model performance.

For example, if a robot model needs to learn kitchen operations, the data company must further assess which tasks to design, how to combine basic actions like grasping, pushing, pulling, opening, and closing, whether deep information is needed, how to arrange cameras, and which failure cases are worth focusing on.

Chris stated that Axis's commercial collaboration with clients like Booster Robotics and Qingtian Technology has evolved from simple data delivery to "customized data production solutions around model training objectives." For instance, in collaboration with Booster Robotics, Axis tailored a foundational model specifically for the Booster T1 that adapts to its visual input and action space. This model supports rapid transfer and iteration of specific tasks with very few samples, significantly lowering the barriers and engineering adaptation costs for embodied intelligence development.

This is also why Axis emphasizes "data engine" rather than "data factory." According to Chris, commercial clients can first propose target scenarios and capability requirements, and Axis will then design tasks based on this, completing data collection through a distributed contributor network.

"In addition to delivering data to clients, AXIS is also conducting academic research on dataset and model validation," Chris noted. The AXIS Franka dataset released by the Axis team earlier this year is one of the largest open-source robot operation datasets for the Franka Research 3 robotic arm, containing 207 diverse manipulation tasks, over 50,000 human demonstration trajectories, and more than 60,000 task scenario variants.

During the training outcome evaluation phase, the research team used this dataset to continuously pre-train the state-of-the-art VLA model π 0.5. Experimental results showed that the model trained with AXIS-100% data snapshots performed excellently in the LIBERO-Plus robustness benchmark test, achieving an overall success rate of 88.8%.

Chris also mentioned the significant effect of the AXIS Franka dataset in enhancing the model's ability to cope with real-world disturbances: under challenges such as sensor noise and camera angle shifts, the model's robustness improved by 13.7% and 11.3%, respectively. The research confirmed that as the dataset snapshot increased from 25% to 100%, the model's manipulation generalization ability exhibited a stable expansion trend, demonstrating a positive correlation between data scale and downstream task generalization ability.

Not Betting on a Single Data Form, Aiming to be the "Surge AI of Robotics"

Robot data remains a rapidly changing market that has yet to form a stable paradigm.

Recently, the industry's focus has gradually expanded from remote operation of real machines to data sources such as simulation and first-person perspectives. As foundational models enter deeper post-training phases, how to collect failure states, error correction processes, and recovery trajectories has also begun to emerge as new data demands.

"If today the market needs first-person perspective data the most, I will only do first-person perspective; what if no one needs it in six months?" Therefore, Chris is reluctant to define Axis as a company that only produces a specific type of data.

His approach is to separate relatively stable underlying capabilities from the ever-changing data collection methods. The foundation consists of infrastructure such as distributed contributor networks, task management, data validation, cleaning, augmentation, and processing pipelines; the upper layer connects different data products and collection methods based on model and client needs.

Currently, Axis has started with web-based simulation data collection and is gradually expanding into first-person perspective data and model post-training. Chris introduced that the latest products have incorporated error correction data collection targeting model biases and failure states, and the team is advancing data collection and processing for post-training methods like DAgger, planning to release the first large-scale Human-Gated DAgger post-training dataset by the end of the year.

In his view, as robot models continue to iterate, the value of data partners will also change. The ability of a data company should not only be measured by how much data it can deliver at once but also by whether it can consistently produce high-quality data, maintain a continuously operating contributor network and data processing tools, and adjust the content and structure of the next round of data based on model performance in a timely manner.

This approach somewhat draws on the development paths of Scale AI and Surge AI, with Chris particularly focusing on Surge AI. He hopes that Axis can become the "Surge AI of robotics" in the future.

In Chris's view, the advantage of Surge AI lies not only in having a large amount of data annotation resources but also in its business consistently keeping pace with changes in model demands, extending from large model post-training to new aspects such as intelligent agent training environments.

This ability to not bind itself to a specific data form but to continuously adjust the data and tools provided as the training methods of Physical AI models change is what Axis truly hopes to replicate.

Utilizing Blockchain for Contribution Incentives and Data Tracking

Unlike other competitors, Axis has a relatively unique label—blockchain.

The production process of robot data itself does not require blockchain; simulation still operates in traditional robotic environments, and model training is no different from other AI companies. Blockchain truly plays a role mainly after data is generated: Axis establishes data IDs for verified data trajectories and records the relationships between tasks, data, and contributors on the Base.

Chris stated that blockchain technology primarily addresses two issues.

The first is contribution incentives. When data is produced by globally distributed users, the quantity and quality of data completed by different users vary. The platform needs to know who contributed what and whether the data truly meets the requirements. For contributors, the core is not about how much they do but who provides genuinely effective data.

The second is source tracking. The current AI model training process resembles a black box. The outside world usually only sees the final model but finds it difficult to know which data contributed to a specific capability and who produced that data. If the relationships between tasks, trajectories, and contributors can be continuously recorded, then when a piece of data is purchased by a client in the future, at least a relatively complete source record can be retained.

Axis has already launched a points system to record and measure user contributions. Chris also mentioned that if tokens are issued in the future, contribution points may become the basis for incentives; if a contributor's data is eventually commercialized, the company is also considering allowing them to participate in revenue sharing.

However, these mechanisms are still in the design and improvement stages. Rather than first building a token economy and then finding application scenarios for it, Chris emphasizes first determining what practical problems blockchain can solve.

"I have never felt that I am a crypto robot company."

In his definition, Axis is primarily a robot data company, and if blockchain can help solve contribution incentives and data source tracking, it will be used in those areas; if it cannot generate actual value, there is no need to introduce it for the sake of a "Crypto+AI" narrative.

Next Phase: Expanding Data Coverage and Entering the Supplier List of Leading Model Companies

For the development in the next 6 to 12 months, Axis's roadmap mainly focuses on three directions: product, community growth, and commercialization.

On the product side, Axis plans to continue expanding the scale of first-person perspective data collection and add UMI data and remote operation data for mobile operation tasks, further broadening the types of data and task scope covered by the platform.

In addition to data collection entry points for ordinary users, Axis also plans to provide task generation APIs and data processing tools to enterprise clients and developers, further productizing some underlying data production capabilities.

Community growth is another key focus. Chris stated that Axis hopes to triple the size of its community in the next six months and further expand into different regional markets. Participants will no longer be limited to ordinary data contributors but will also aim to attract more developers, researchers, and industry practitioners to gradually expand the platform into an open ecosystem centered around Physical AI data production.

In terms of commercialization, Axis has already gained benchmark clients in some niche areas, and simulation data and first-person perspective data have already generated paid orders. The company's goal for the next six months is to increase its annual recurring revenue (ARR) to between $500,000 and $1 million and enter the supplier list of leading robot model companies.

Compared to the revenue figures themselves, entering the supply system of leading model companies may better test Axis's current model.

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