Embodied Artificial Intelligence (EAI) Robot Data Industry Layout Research Report, 2026

EAI Robot Data Research: The market size surged by 203% in 2025 with the top ten list being released

In the evolution of embodied artificial intelligence (EAI), high-quality data has been recognized by industry and academia as the core element for crossing the general fine-operation gap. As the hardware ontology gradually matures, the bottleneck of algorithm iteration will be fully shifted to the data side in 2026. How to obtain physically realistic multi-modal data at low cost and on a large scale has become the key to determining the commercialization of EAI in the next five years.

In view of this, ResearchInChina released the Embodied Artificial Intelligence (EAI) Robot Data Industry Layout Research Report 2026. The report researches, analyzes and sorts out the technology evolution and business layout of 24 Chinese EAI data companies in this field, and systematically dismantles the core trends, competitive landscape and business model evolution of the current EAI data arena.

China leads the world in growth rate and remains the largest single market of EAI data.

After laboratory exploration and preparation for commercialization, the EAI data arena officially saw the first year of large-scale commercialization in 2025. The total global market size hit over USD242 million in 2025, a year-on-year increase of 181.4%. The compound annual growth rate (CAGR) of the global market from 2025 to 2030 will reach 85.0%, and the total size will climb to USD5.25 billion in 2030.

From the perspective of the macro development curve, the entire market shows significant exponential growth. This outbreak is not driven by a single factor, but is the result of the resonance between ontology companies, scientific research institutions, and third-party data providers on the underlying infrastructure. After entering the first year of commercialization, the core demand of the industry has rapidly transferred from teleoperation laboratory construction to procurement of standardized massive training data.

In the global EAI data industry, the growth momentum of the Chinese market is extremely strong. In 2025, China's total EAI data market size hit RMB500 million, with a year-on-year growth rate of 203%, nearly 20 percentage points higher than the global average for the same period. Thanks to China's huge manufacturing base and rich commercial scenarios, the proportion of China's EAI data in the global market has remained stable at as high as 40%.

As per the market structure, the Chinese market is currently in the stage of rapidly deploying data collection hardware. At this stage, a large amount of budget in the Chinese market flows to digital collection hardware equipment such as motion capture suits, force feedback gloves, and ontology-free collection brackets. Data collection equipment and robots take an overwhelming share in the overall market. Although pure data services (DaaS) are rapidly sprouting, they are currently mainly serving customized small-batch annotation and collection orders, without a dominant standardized delivery system.

Although hardware sales remain the core monetization method at present, the value creation logic of the industrial chain is undergoing a fundamental restructuring. As the scale effect of data accumulation becomes evident, the marginal cost per data unit will drop sharply, and the industry's competitive moat will fully shift from hardware manufacturing to data asset operation.

Major leading companies are stepping up efforts to build exclusive data factories and joint training venues, trying to seize data pricing power in the future redistribution of the value chain. A competition around high-value and high-quality data sets has begun.

The top 10 on the list form distinct tiers, with national public platforms, ontology companies, and third-party unicorns competing equally.

Through quantitative evaluation of six dimensions (data scale and capacity, technological foundation, dataset influence, simulation capabilities, and commercialization), the top 10 in the Chinese EAI data sector have revealed a clear division of tiers.

As the top three, Lightwheel, National and Local Co-Built Humanoid Robotics Innovation Center and AGIBOT represent the distinct three types of successful players: independent data providers, national public platforms, and full-stack ontology companies. National public platforms leverage policy and scenario resources to strongly coordinate standards, while unicorn companies build high barriers in specific data modalities through extreme technological vertical integration.

The competitive edge of Lightwheel, a unicorn in this field, lies in its extremely high data generation efficiency and zero-marginal-cost scalability. The company masters a full-stack self-developed physical simulation engine. Its EgoSuite released in December 2025 has delivered more than 300,000 hours of data and is producing more than 20,000 hours of data every week. With the support of its cross-ontology data mapping and industrial-grade evaluation benchmarks (RoboFinals), Lightwheel has not only solved the domain gap of Sim2Real, but also won the customers of 80% of the world's top EAI teams with extremely high technical barriers.

AGIBOT and UBTECH, typical complete robot companies, choose a strategic closed loop with high coupling of ontology-data-model-scenario. AGIBOT has invested in building a 4,000-square-meter super data factory in Pudong, Shanghai, and deployed nearly a hundred AGIBOT A2-D robots to achieve extremely high-speed data collection of 1,000 data entries per robot per day.

The sixth-ranked PaXini provides the industry with a differentiated solution. Amid the fierce competition in the visual and trajectory data market, PaXini has built a full-modal EAI production line with an annual capacity of nearly 200 million entries, centered on multi-dimensional tactile sensing. Its Super EID Factory achieves precise alignment through 6D Hall array dexterous hands and a multi-view vision matrix, addressing the demand for contact mechanics data in industrial precision assembly, 3C manufacturing, and other fields.

Third-party service providers such as WUWEN.AI, TARS and GenRobot.AI, which rank at the top of the list, have all embarked on ecosystem alliance. TARS's human-centric four-modal data collection is deeply bound to scenario parties such as Kupas; WUWEN.AI has built a full-domain open scenario in the Yangtze River Delta, uniting dozens of upstream and downstream institutions in the industry chain.

Physical simulation engines form a core competitive moat, with Lightwheel leading the global synthetic data and evaluation ecosystem.

Chinese companies represented by Lightwheel have occupied more than half of the global simulation synthetic data segment. Lightwheel itself has seen explosive revenue growth, with the revenue exceeding RMB100 million in 2025, and the revenue in the first quarter of 2026 more than that in the whole year of 2025.

The core moat of Lightwheel is reflected in three dimensions:
The first is the high fidelity and generation efficiency of the underlying engine. Lightwheel's simulation engine can accurately simulate physical properties such as software, fluids, and multi-body complex contacts, greatly bridging the domain gap of Sim2Real (simulation to reality).

Secondly, Lightwheel has built a large-scale non-ontology data engine, covering the two major paths of simulation synthetic data and human video data (EgoSuite), to achieve large-scale production of EAI data. Its data solutions have been delivered on a global scale, and its production capacity continues to lead the industry.

Finally, it boasts strong platform engineering capabilities. Its simulation evaluation platform RoboFinals has built 100 difficult tasks and scenarios, covering real application environments such as homes, factories, and supermarkets. All tasks are derived from real needs to ensure alignment with the real world and support large-scale evaluation. Isaac Lab-Arena is an industry-grade large-scale evaluation platform for basic robot models. It introduces real-world task definitions and evaluation standards and has been used by many top model teams such as Alibaba Qwen for internal evaluation.

The most critical thing is its say in global ecological standards. Lightwheel has not only joined the internationally authoritative Newton TSC and participated in the development of the SimReady digital asset standard, but also launched the industry's first industry-grade benchmark, RoboFinals. Currently, 80% of the world's top EAI R&D teams (NVIDIA, Google, DeepMind, etc.) are using its datasets and platform services.

Multi-source fusion collection solutions are becoming an inevitable trend, and complementary advantages are reshaping the data production pipeline.

Teleoperation, as the current gold standard for acquiring high-quality real-device data, can perfectly preserve the implicit decisions and real force feedback of humans during operation. However, this 1:1 mapping technology faces an extremely steep cost curve. Taking the construction of a medium-sized data collection plant as an example, the motion capture suit, force feedback gloves, and high-degree-of-freedom body alone can easily cost hundreds of thousands of yuan per set of hardware. Calculations show that the cost of a single valid data entry in traditional teleoperation is over RMB8, and the daily production capacity of a single robot is only around 1,000 entries.

In stark contrast to teleoperation is the explosive growth of simulation synthesis technology. Relying on the stack of computing power, the simulation engine can continuously generate long-tail data containing extreme working conditions in a virtual environment 24 hours a day, and the cost of a single entry of data is extremely compressed to millimeters.

For example, Galbot can generate hundreds of millions of operational data sets within a week by virtue of a simulation platform. However, seemingly unlimited simulation data is always subject to the domain gap (virtual-real gap). The simplification of physical parameters such as mechanics, contact, and friction makes pure simulation models easily distorted when directly transferred to the physical world. Therefore, the integration paradigm of “90% simulation pre-training + 10% real robot fine-tuning” has become the current engineering optimization solution.

Moreover, in order to balance authenticity and collection costs, ontology-free/light-ontology data collection technology represented by UMI (Universal Manipulation Interface) emerged in 2025. The FastUMI Pro handheld collection system launched by Lumos Robotics replaces the traditional laser base station with pure visual SLAM positioning, which not only compresses the collection time from 50 seconds to 10 seconds for a single data entry, but also reduces the underlying cost to RMB0.5. More importantly, UMI realizes the complete decoupling of data and robot hardware. Ordinary collectors can complete millimeter-level precision operational data recording in real homes or factories, allowing data collection to truly go out of the laboratory.

As foundation models drive an exponential expansion in data demand, a single technical approach can no longer meet the stringent requirements of scale, cost, precision, and generalization. The industry is fully entering an era of multi-source integrated collection: general physical knowledge is injected through human videos, long-tail boundaries are massively covered by synthetic simulation data, real interactive actions are distributed and expanded via UMI collection, and finally expert-level fine-tuning in vertical scenarios is carried out relying on high-precision teleoperation.

Data circulation models are evolving towards standardization and platformization; data supermarkets and compliant exchanges are accelerating their evolution.

As EAI moves from R&D to application, the way the industry acquires data is undergoing a profound restructuring of its business model. The past business model of one customer, one collection; highly customized; and lengthy cycle is rapidly evolving towards standardization, platformization, and DaaS.

First, the data supermarket model emerges. Lumos Robotics is a pioneer of this model. In March 2026, it launched the industry's first FastUMI Pro Data Store. Lumos Robotics is not limited to taking customization orders, but subdivides the EAI data of the ten core scenarios such as industrial manufacturing, hotel services, and family life into dozens of standardized operation tasks, and puts them directly on the official website for sale. Users can purchase multi-modal data sets covering vision, posture, force perception, etc. just like purchasing standard hardware products.

Second is the implementation of the “cloud data mall” model. PaXini teamed up with Tencent Cloud to create the EAI Data Cloud Mall. This model deeply unbinds huge multi-modal tactile data sets and cloud computing power. Customers do not need to build their own local computing servers and storage clusters, and can directly perform data screening, format conversion and model adaptation training in the cloud. One-click online delivery of standardized data packages completely opens up the closed loop of massive data supply - cloud computing power scheduling - efficient model training.

The most critical thing is that the “data exchange” has opened up the “last mile” of compliance assetization. EAI real scenario data involves complex intellectual property rights, privacy desensitization and environmental ownership issues. At present, national hubs such as the Jiangsu Data Exchange and the Beijing International Data Exchange have taken the lead in breaking through the situation. For example, the Jiangsu Data Exchange completed the country's first on-site transaction of an EAI data set (a 25,000-entry four-scenario data set developed by Jiangsu Truejing Intelligent Technology); the Beijing International Data Exchange officially launched PaXini's OmniSharing DB full-modal data set.


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