World Models Are About to Become Standard, and OEMs Enter and Accelerate Mass Production and Application
Company Logo Dublin, May 13, 2026 (GLOBE NEWSWIRE) — The “Embodied AI Robot Large Model (Including VLA) Research Report, 2026” report has been added to ResearchAndMarkets.com’s offering. Research on Robot Large Models: World Models Are About to Become Standard, and OEMs Enter and Accelerate Mass Production and Application The analyst has released the Embodied AI…
Dublin, May 13, 2026 (GLOBE NEWSWIRE) — The “Embodied AI Robot Large Model (Including VLA) Research Report, 2026” report has been added to ResearchAndMarkets.com’s offering.
Research on Robot Large Models: World Models Are About to Become Standard, and OEMs Enter and Accelerate Mass Production and Application
The analyst has released the Embodied AI Robot Large Model (Including VLA) Research Report, 2026, which focuses on the research, analysis, and summary of the following content:
The basic concepts, industrial ecosystem map, multi-dimensional classification (application scope, capability modality, architecture), industry development drivers, key technology development directions, and commercialization modes of Embodied AI robot large models;
The layout planning, team building, core talents, large model products and their applications, detailed introduction and implementation status of Embodied AI robot large model products, Embodied AI ecosystem partners, and recent key dynamics of 11 tech giants in the Embodied AI robot field, including Alibaba Group, NVIDIA, Google DeepMind, OpenAI, Microsoft, Huawei, Tencent RoboticsX, Baidu, ByteDance, iFlytek, and SenseTime;
The profile, development history and planning, robot products and large model installation, detailed introduction of self-developed large models, large model ecosystem cooperation, and recent key dynamics of 10 well-known robot enterprises, including UBTECH Robotics, Unitree Robotics, AgiBot, Leju Robotics, Galbot, RobotEra, FigureAI, Sanctuary AI, 1X Technologies, and Neura Robotics;
The layout planning, team building, core talents, robot products and large model installation, summary of large model products, detailed introduction of Embodied AI robot large model products, Embodied AI ecosystem partners, and recent key dynamics of 11 OEMs in the Embodied AI robot field, including Tesla, Toyota, Honda, Hyundai, Xiaomi, XPeng, GAC Group, Chery, Leapmotor, BYD, and Dongfeng Motor. In addition, this report summarizes the layout of 13 other global OEMs in Embodied AI robot field.
Embodied AI robot large models (“robot large models” for short) can make end-to-end or hierarchical decisions compared with traditional robot control algorithms, without the need for precise modeling, and can operate in unstructured and open environments (families, outdoors, cluttered desktops). Compared with general large models, Embodied AI robot large models pay more attention to the fusion and understanding of multi-modal information (vision + lidar + touch + text, etc.), aiming to complete closed-loop actions in the physical world and output motion commands such as joint angles, speeds, and grasping forces.
In recent years, Embodied AI robot large model field has shown the following development trends:
Embodied AI Players Have Begun to Apply World Models
Currently, robot large models represented by Vision-Language-Action (VLA) models have made significant progress in the “perception-decision-execution” closed loop, enabling robots to understand instructions and generate actions. However, such models still face bottlenecks in coping with the high diversity and uncertainty of physical world. In essence, they are more like “imitating” patterns in training data, lacking the foresight of action consequences and the understanding of physical logic.
Driving forces for the application of world models mainly come from three aspects:
Solving the data bottleneck: The collection of high-quality real robot data is extremely costly and limited in scale, having become a core constraint on capability upgrading. World models can serve as powerful “data generators” and “simulation engines”, generating massive, controllable, and high-fidelity synthetic training scenarios, and greatly reducing the reliance on expensive real robot data.
Improving decision and generalization capabilities: Through prediction and deduction, world models enable robots to have a certain degree of causal reasoning and physical intuition, capable of handling new scenarios and new objects not seen in training, and achieving “learning by analogy”.
Realizing the collaborative evolution of “cerebrum” and “cerebellum”: The industry consensus is that future robots’ intelligence will be the result of collaborative evolution of the “cerebrum” (high-level cognition and planning) and the “cerebellum” (low-level motion control). As a key component of the high-level “cerebrum”, the world model forms a complementary relationship with execution-oriented models such as VLA, jointly constituting a complete intelligent system.
Robot Large Models Achieve Cross-Platform Applications
In traditional robot development mode, the software and algorithms of each robot need to be specially developed and optimized for its unique hardware configuration (sensors, actuators, form), leading to high R&D costs, long cycles, and non-reusable capabilities. The cross-platform application of robot large models can break this drawback. By building a powerful end-to-end multi-modal foundation model, it implants transferable general intelligence into robots, enabling them to cross the limitations of different ontologies (such as humanoid, quadruped, robotic arm), different tasks and different environments, and realize rapid generalization and deployment of capabilities.
Starting from 2025, robot large models such as NVIDIA’s GR00T series, Google DeepMind’s Gemini Robotics, Microsoft’s Rho-alpha, Huawei’s CloudRobo, and RobotEra’s ERA-42 all support cross-robot platform development and cross-scenario applications.
An Increasing Number of Robot Large Models Are Open-Sourced
The open-sourcing of large models is not a simple technical sharing. Open-source models gather the wisdom of global developers and can quickly overcome complex “long-tail problems” in the physical world. At the same time, open-sourcing breaks traditional closed-source business mode, allowing small and medium-sized enterprises to quickly develop based on open-source models, focus resources on hardware innovation and implementation in scenarios, and form an industrial pattern of “giants build the platform, and hundreds of enterprises perform on it”.
The core of open-sourcing is to lower the R&D threshold, accelerate technological iteration, build ecosystem barriers, promote large-scale implementation, and form a positive flywheel of “open-source – ecosystem – data – more powerful models”.
OEMs Enter the Market to Solve the Scarcity of Real Data for Embodied AI Robot Large Models and Provide Field Verification Scenarios
The entry of multiple OEMs into the Embodied AI and humanoid robot track brings massive industrial scenario data, automotive-grade sensor data and a mature autonomous driving technology stack to Embodied AI large models (VLA, world models, etc.). Algorithms such as BEV perception, multi-modal fusion, and end-to-end decision can be directly migrated to robots to train and improve environmental understanding, task planning and motion control capabilities of models. The production line scenarios of OEMs can verify the reliability and success rate of robot large models, expose model defects at the same time, provide high-reliable real robot interaction data for future model correction, and effectively narrow the large gap between simulation and reality.
In addition, OEMs introduce automotive-grade safety standards and hardware collaborative design into robots, greatly optimizing the reasoning delay, reliability and implementation efficiency of large models; the core supply chains of automobiles and robots (batteries, motors, sensors, domain controllers, etc.) have a high degree of overlap. Some institutions estimate that the overlap rate exceeds 50%. The scale effect greatly reduces the cost of core hardware, and the model deployment cost also decreases synchronously.
Key Topics Covered:
1 Overview of Embodied AI Robot Large Models and Key Technology Development Directions 1.1 Core Definitions of Embodied AI Robot Large Models 1.2 Global Industrial Ecosystem Map of Embodied AI Robot Large Models 1.3 Classification of Embodied AI Robot Large Models 1.4 Industry Development Drivers of Embodied AI Robot Large Models 1.5 Key Technology Development Directions of Embodied AI Robot Large Models 1.6 Commercialization Modes of Embodied AI Robot Large Models
2 Global Major Players and Products: Tech Giant Camp 2.1 Summary of Typical Embodied AI Large Model Products of Tech Giants (1)-(3) 2.2 Alibaba Group 2.3 NVIDIA 2.4 Google DeepMind 2.5 OpenAI 2.6 FOURIER 2.7 Huawei 2.8 Tencent RoboticsX 2.9 Baidu 2.10 ByteDance 2.11 iFlytek 2.12 SenseTime
3 Global Major Players and Products: Robot Enterprise Camp 3.1 Summary of Typical Embodied AI Large Model Products of Robot Enterprises (1)-(3) 3.2 UBTECH Robotics(UBTECH) 3.3 Unitree Robotics 3.4 AgiBot 3.5 Leju Robotics 3.6 Galbot 3.7 RobotEra 3.8 FigureAI 3.9 Sanctuary AI 3.10 1X Technologies 3.11 Neura Robotics
4 Global Major Players and Products: Cross-Border OEMs Camp 4.1 Summary of Typical Embodied AI Large Model Products of OEMs (1)-(4) 4.2 Tesla 4.3 Toyota 4.4 Honda 4.5 Hyundai 4.6 Xiaomi 4.7 XPeng 4.8 Synapath AI 4.9 Chery 4.10 Leapmotor 4.11 BYD 4.12 Dongfeng Motor 4.13 Summary of Global Other Main OEMs’ Layout in the Embodied AI Robot Field (1)-(4)
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