EAI(Embodied Artificial Intelligence) 로봇 데이터 산업 레이아웃(2026년)

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.

Table of Contents

1 Core Technology System and Status Quo of EAI Robot Data

• 1.1 EAI Robot Data Policies
• EAI Robot Data Policies - National Level
• EAI Robot Data Policies - Local Level (1)
• EAI Robot Data Policies - Local Level (2)
• National Policy Support for EAI Robot Data Factories
• Local Policy Support for EAI Robot Data Factories
• Artificial Intelligence EAI Data Closed-Loop Management Specifications
• 1.2 Status Quo of EAI Data Development
• EAI Data Industry Chain
• Core Path of EAI Data - Imitation Learning + High-Quality Data
• Detailed Explanation of EAI Data Pyramid
• EAI Data Management Measures
• Global EAI Data Market Size, 2023-2030E
• China's EAI Data Market Size, 2023-2030E
• 1.3 Analysis on Ranking of EAI Data Enterprises
• Analysis on Ranking of EAI Data Enterprises
• Overall Ranking of EAI Data Enterprises (1)
• Overall Ranking of EAI Data Enterprises (2)
• Overall Ranking of EAI Data Enterprises (3)
• Ranking of EAI Data Enterprises by Data Scale, Capacity and Quality (1)
• Ranking of EAI Data Enterprises by Data Scale, Capacity and Quality (2)
• Ranking of EAI Data Enterprises by Data Scale, Capacity and Quality (3)
• Ranking of EAI Data Enterprises by Data Scale, Capacity and Quality (4)
• Ranking of EAI Data Enterprises by Data Technology Foundation and End-to-End Capability (1)
• Ranking of EAI Data Enterprises by Data Technology Foundation and End-to-End Capability (2)
• Ranking of EAI Data Enterprises by Data Technology Foundation and End-to-End Capability (3)
• Ranking of EAI Data Enterprises by Data Technology Foundation and End-to-End Capability (4)
• Ranking of EAI Data Enterprises by Dataset Products and Their Industry Influence (1)
• Ranking of EAI Data Enterprises by Dataset Products and Their Industry Influence (2)
• Ranking of EAI Data Enterprises by Dataset Products and Their Industry Influence (3)
• Ranking of EAI Data Enterprises by Simulation and Synthetic Data Capability
• Ranking of EAI Data Enterprises by Commercialization and Customer Ecosystem (1)
• Ranking of EAI Data Enterprises by Commercialization and Customer Ecosystem (2)
• Ranking of EAI Data Enterprises by Commercialization and Customer Ecosystem (3)
• Ranking of EAI Data Enterprises by Financing and Industry Resources
• 1.4 EAI Data Collection Methods
• Four Mainstream Routes for EAI Data Collection
• Teleoperation
• Introduction to Four Mainstream Teleoperation Devices
• Introduction to Master Devices for Robot Teleoperation
• Complete Data Collection Process for Robot Teleoperation
• Cost Structure of Robot Teleoperation
• Core Characteristics of Robot Teleoperation Data
• Considerations for Robot Teleoperation Data Collection
• Robot Teleoperation Data Format
• HMI Objectives in Robot Teleoperation
• Role, Advantages, and Disadvantages of EAI Motion Capture in Robot Teleoperation
• Simulation Synthesis
• Status Quo of EAI Simulation Environment Data Generation Technology
• Principle and Process of EAI Simulation Technology
• Data Simulation: Status Quo of Large-Scale Collection Technology for Native Operation Data
• Data Synthesis: Status Quo of Generative AI-Driven Full-Chain Data Generation Technology
• Detailed Explanation of Some Mainstream Simulation Platforms
• Advantages and Disadvantages of EAI Simulation Synthesis Data
• Human Video Data
• Core Definition and Boundary of Human Video Data Collection for EAI
• Entire Process Technical Pipeline of Human Video Data Collection for EAI
• Core Advantages and Differentiated Competitiveness of Human Video Data Collection for EAI
• Development Trends and Evolution of Human Video Data Collection for EAI
• Tesla Optimus Uses Human Video Data as Core Training Fuel
• UMI
• Technical Principle of UMI-based EAI Data Collection
• Core Features of UMI-based EAI Data
• UMI-based EAI Data Collection Hardware Composition and Process
• Cost Comparison between UMI-based Data Collection and Teleoperation Collection
• UMI Ecosystem Evolution
• Core Capabilities and Shortcomings of UMI-based Data and EGO Data
• Complementary Capabilities of UMI-based Data and EGO Data
• 1.5 Data Trading Platforms
• Full-Process Trading System of EAI Data Exchanges
• Layout and Milestones of Core Exchanges (1)
• Layout and Milestones of Core Exchanges (2)
• Layout and Milestones of Core Exchanges (3)
• Current Core Problems and Development Bottlenecks of EAI Data Exchanges

2 EAI Robot Data Ecosystem Construction and Future Development Trends

• 2.1 Benchmarking Analysis of EAI Data Enterprises
• Benchmarking Analysis of Core EAI Data Technology Roadmaps and Ecosystem Layout Capabilities (1)
• Benchmarking Analysis of Core EAI Data Technology Roadmaps and Ecosystem Layout Capabilities (2)
• Benchmarking Analysis of Core EAI Data Technology Roadmaps and Ecosystem Layout Capabilities (3)
• Benchmarking Analysis of EAI Data Collection Hardware and Collection Solutions (1)
• Benchmarking Analysis of EAI Data Collection Hardware and Collection Solutions (2)
• Benchmarking Analysis of EAI Data Collection Hardware and Collection Solutions (3)
• Benchmarking Analysis of EAI Data Infrastructure and Capacity Scale (1)
• Benchmarking Analysis of EAI Data Infrastructure and Capacity Scale (2)
• Benchmarking Analysis of EAI Dataset Products and Core Data Assets (1)
• Benchmarking Analysis of EAI Dataset Products and Core Data Assets (2)
• Benchmarking Analysis of EAI Dataset Products and Core Data Assets (3)
• Benchmarking Analysis of EAI Data Commercialization Capabilities and Business Models (1)
• Benchmarking Analysis of EAI Data Commercialization Capabilities and Business Models (2)
• Benchmarking Analysis of EAI Data Commercialization Capabilities and Business Models (3)
• 2.2 Construction of EAI Robot Data Factories
• Regional Layout of EAI Robot Data Factories
• Development Trends of EAI Robot Data Factories
• Business Models of EAI Robot Data Factories
• PaXini EAI Super Data Collection Factory
• Real-time Data Status of PaXini Super Data Collection Factory
• AGIBOT EAI Data Collection Factory
• AGIBOT's Data Collection Hardware and Quality Control
• AGIBOT's Data Services
• Yangtze River Delta (Deqing) EAI Data Collection Factory
• Wuxi EAI Robotics Industrial Data Collection and Training Center
• EAI Data Training Base of Beijing Innovation Center of Humanoid Robotics
• 2.3 EAI Data Development Trends
• Trend 1: Accelerated Maturation of "Ontology-Free" or "Light-Ontology" Data Collection Technologies
• Trend 2: Formation of Dual-Engine Data Production Model, Large-Scale Application of "Real Data + Simulation Data" Integration Paradigm
• Trend 3
• Trend 4
• Trend 5
• Trend 6
• Trend 7
• Trend 8
• Trend 9

3 Data Layout of EAI Robot Ontology Companies

• 3.1 AGIBOT
• Profile
• EAI Full-Stack Data Collection Solution
• Industrial-Grade "Sim-to-Real" Data-Driven Closed-Loop Engine
• EAI Full-Stack Data Collection System Analysis
• EAI Full-Stack Data Collection Solution - Data Collection Ontology (1)
• EAI Full-Stack Data Collection Solution - Data Collection Ontology (2)
• EAI Full-Stack Data Collection Solution - Teleoperation Devices
• EAI Full-Stack Data Collection Solution - Data Collection Platform
• Details of EAI Data Collection Platform Process
• Dataset
• Open Source Simulation Platform
• 3.2 PaXini
• Profile
• Cooperation with Tencent Cloud in EAI "Data Cloud Mall".
• Full-Modal Data Collection System
• Full-Modal EAI Dataset
• Business and Technology Closed Loop of Super ElD Factory
• Cross-Ontology Data Mapping Solution
• 3.3 Galbot
• Profile
• EAI Data Collection Route
• Suzhou EAI Data Collection Center
• Dexterous Hand Functional Grasping Synthetic Large Dataset
• Dexterous Hand Grasping Dataset
• Cross-Ontology Full-Domain Surround View Navigation Foundation Model
• Training Data System
• Component Benchmark Dataset
• 3.4 Galaxea AI
• Profile
• EAI Data Business Model
• EAI Data Collection Path
• RSR Truth Reconstruction Technology
• Galaxea Open-World Dataset
• Open World Dataset Data Classification and Characteristics
• Data Zero/Low-Annotation Technology
• Data Collection Hardware - R1 Lite
• R1 Teleop Homogeneous Teleoperation Platform and R1 VR Teleoperation Device
• EAI Development Platform - Embodiment Human Interface
• 3.5 Lumos Robotics
• Profile
• Integrated Closed-Loop Capability of "Collection-Training-Promotion"
• EAI Data Collection Equipment
• Core Technology
• Backpack-Version UMI-based Data Collection Equipment
• Application Case
• Data Collection Equipment
• Data Supermarket
• 3.6 FOURIER
• Profile
• Core EAI Data Collection Layout
• Open Source Full-Size Humanoid Robot Dataset
• Data Collection Ontology
• EAI Data Full-Process Toolchain
• EAI Data Application Foresight - Brain-Computer Interface
• 3.7 Beijing Innovation Center of Humanoid Robotics
• Profile
• EAI Data Closed-Loop One-Stop Platform
• Dataset
• Data Collection Characteristics (1)
• Data Collection Characteristics (2)
• Data Collection Strategy
• Data Management and Quality Inspection
• High-Fidelity Dataset of Articulated Object Digital Assets
• 3.8 Leju Robotics
• Profile
• Strategic Cooperation in the Data Field
• Two Major Industrial Scenario Datasets Successfully Obtained Jiangsu Province Data Intellectual Property Registration Certificates
• Real Embodied Humanoid Robot Data Solution
• Comprehensive Construction Solution for Training Grounds
• Core Technology Base for EAI Data Collection
• Training Ground Operation Service Solution
• Real Full-Size Humanoid Robot Dataset
• Dexterous Maneuver Dataset
• 3.9 UBTECH
• Profile
• Humanoid Robot Multimodal Data Collection and Testing Center
• EAI Data Collection Solution - Scenario
• EAI Data Collection Solution - Collection, Processing and Application
• EAI Foundation Model Data Conversion Process
• EAI Foundation Model Data Feedback Mechanism
• Four Datasets
• Virtual Simulation Platform
• 3.10 LimX Dynamics
• Introduction
• EAI Training Paradigm Based on Multivariate Data
• Workflow
• EAI Robot Operation Algorithm Based on Video Data
• Core Technical Features
• Training Paradigm

4 Layout of EAI Data Providers

• 4.1 Noitom
• Profile
• Data Factory Solution
• Training Data Production Mode
• Detailed Explanation of Training Data Production Mode
• Building an EAI Data Factory Using NVIDIA Isaac and Motion Capture
• Collaboration with AGIBOT
• 4.2 GigaAI
• Profile
• Embodied World Model
• EAI Robot
• Co-building an EAI Data Factory with Hubei Humanoid Robotics Innovation Center
• Embodied Foundation Models
• 4.3 GenRobot.AI
• Profile
• Product Matrix
• Full EAI Data Collection Process
• Ontology-Free Embodied Dataset
• Data Collection Equipment (1)
• Data Collection Equipment (2)
• Data Processing Platform
• EAI Data Production Line
• 4.4 National and Local Co-Built Humanoid Robotics Innovation Center EAI data
• EAI Data Collection Layout
• "Shanghai Virtual-Real Fusion EAI Training Ground" National Standardization Pilot Project Settled down in Shanghai
• "1+N" EAI data Training Ground Layout
• Dataset Receives Quality Assessment Certificate from CAICT
• Participation in National and Local EAI Data Policy Formulation
• Development of China's First EAI Dataset Quality Evaluation Industry Standard with CAICT as Co-leader
• EAI data Collection and Training Process Closed Loop
• Detailed Process Analysis
• "White Tiger" Heterogeneous Robot Dataset
• First Open Source v1.0 of "White Tiger" Heterogeneous Robot Dataset
• Data Distribution Proportion of "White Tiger" Heterogeneous Robot Dataset
• Atomic Skills and Toolchain of "White Tiger" Heterogeneous Robot Dataset
• High Unification and Multiplexing Design of "White Tiger" Heterogeneous Robot Dataset
• Core Features of "White Tiger" Heterogeneous Robot Dataset
• Collection Process of "White Tiger" Heterogeneous Robot Dataset
• "White Tiger" Cross-Ontology Vision-Haptic Multimodal Dataset
• Cross-Ontology Vision-Haptic Multimodal Dataset
• Data Types Covered by White Tiger-VTouch Dataset
• Technical Features of Cross-Ontology Scalable Data Collection Platform
• Unified Algorithm Framework for Real-Robot Model Training and Induction
• White Tiger Assists in the Construction of Next-Generation Embodied VTLA and Training Ground Standards
• Real Full-Size Humanoid Robot Dataset
• Humanoid Robot Dataset for Real Scenarios and Long-Term Tasks
• Core Features of Dataset
• Scenario Distribution and Technological Innovation of Dataset
• 4.5 Jiangsu Truejing Intelligent Technology
• Profile
• Participation in Building Jiangsu Provincial Humanoid Robot Data Collection Center
• Data Collection Ontology
• Data Infrastructure and Collection Capability Construction
• Dataset Listed and Traded on Jiangsu Data Exchange
• 4.6 TARS
• Profile
• Construction of "EAI data Spark Program" with Kupas
• Data Collection Paradigm
• Data Collection Suite (1)
• Data Collection Suite (2)
• EAI Data Engine
• Scalable Real Embodied Multimodal Dataset
• Core Parameters of Dataset
• World Model Built from Data
• 4.7 Noematrix
• Profile
• Strategic Cooperation with coScene to Jointly Develop a Heterogeneous Robot Training Ground Solution
• RoboPocket
• Data Quality Control Mechanism of RoboPocket
• One-Stop EAI Development Toolchain
• Accompanying Data Collection System
• Atomic Skill Library
• 4.8 Synapath AI
• Profile
• Open Source EAI Dataset
• Video-based EAI Data Solution
• Video Processing Engine for EAI Data Collection
• 4.9 WUWEN.AI
• Profile
• 10,000-Hour Open Source Dataset Project of Parnership among Sweet Potato Robot, Horizon Robotics and WUWEN.AI
• EAI's Three Major Data Layouts
• EAI Full-Link Data Synthesis Solution
• EAI Data Digital Asset Construction
• Physical AI Data Base Platform
• EAI Data Collection and Training Ground
• Physical AI Simulation Platform
• Multimodal Automated Data Annotation Platform

5 Layout of EAI Data Service and Simulation Enterprises

• 5.1 Simulation Platform
• EAI Robot Simulation Platform (1)
• EAI Robot Simulation Platform (2)
• EAI Robot Simulation Platform (3)
• 5.2 IO-AI Tech
• Profile
• Data Platform
• Functional Modules of Data Platform
• EAI Data Software Platform Services
• Data as a Service
• Third-Generation Robot Teleoperation Data Collection System
• Parameters of Robot Teleoperation Data Collection System
• Real-World Data Collection System
• 5.3 LightWheel AI
• Profile
• Participation in the Technical Steering Committee of Newton
• Core Product Structure
• Human Data Solution for EAI and World Model
• Simulation Environment Assets
• Industrial-Grade Embodied Simulation Evaluation Platform
• Full-Stack Embodied Evaluation System
• 5.4 PsiBot
• Profile
• Human Data Collection System
• Portable EAI Data Collection Kit
• One-Stop Data Collection Platform
• Dataset
• 5.5 Appen
• Profile
• Integrated Data Collection Solution
• EAI Data Products, Services and Deliverables
• Dataset Products
• EAI Data Development Platform
• 5.6 ORCA
• Profile
• ORCA supports the landing of the first EAI data hub in the Greater Bay Area in Longgang, Shenzhen
• All-in-one Simulation System
• Technical Capabilities of All-in-one Simulation System
• Practical Application Cases of ORCA System
• One-stop Industrial EAI Simulation Training Solution
• Physics AI Personal Developer Platform