The "OEMs and Suppliers' Embodied Artificial Intelligence (and AI Robot) Layout Trend Report, 2024-2025" report has been added to ResearchAndMarkets.com's offering.
From Automobiles to Embodied Artificial Intelligence (EAI): Differentiated Layout Strategy amid Industrial Correlation
EAI Layout: Multiple Paths of OEMs and Suppliers
In the wave of Embodied Artificial Intelligence (EAI), OEMs and suppliers in the automotive industry chain have set foot in the industry and vigorously explored development strategies that suit them. At present, they deploy EAI by way of independent R&D, cooperative R&D, investment and cooperative application exploration. Companies need to choose their specific layout models, each of which has its own advantages and disadvantages, according to their own resources, technical strength and market goals.
Simply put, EAI is an agent that perceives and acts based on the physical body. It is like giving a robot a 'body' that can interact with the environment, so that it can learn and perform tasks by observing, moving, and speaking like humans. The concept has brought unprecedented opportunities and challenges to the automotive industry.
With coherent technological development, OEMs are accelerating the deployment of humanoid robots
OEMs' EAI deployment focus on technology reuse, supply chain collaboration, and market incremental mining. Moreover, intelligent driving technology (such as perception algorithms and decision-making models) is highly homologous to EAI. OEMs can draw the AI capabilities they have accumulated in the field of autonomous driving on robot development to reduce R&D costs. In the future, as hardware costs decline and foundation model capabilities improve, EAI will gradually penetrate from industrial scenarios into the consumer sector, becoming a key pillar for the intelligent transformation of OEMs.
Suppliers are making every effort to deeply lay out the EAI industry chain
Automotive suppliers rely on existing hardware technologies (such as sensors, chips, motors) and supply chain resources to extend into the field of EAI. Their core logic lies in technology reuse and collaborative cost reduction. For example, the LiDAR vendor RoboSense has adapted its automotive perception solution to robots so as to migrate its environment modeling capabilities; motor companies have reused automotive powertrain technology to develop high-density joint drive modules. This move can leverage mature manufacturing experience and customer network, but it has to address the stringent requirements of robotic scenarios for hardware flexibility.
Technical talents in the field of autonomous driving dabble in EAI
Similar technical paths and industry dividends stimulate autonomous driving practitioners to dabble in EAI. After L4 autonomous driving encountered obstacles in commercialization, capital and talent shifted to EAI. Autonomous driving and EAI highly overlap in perception algorithms and decision models (end-to-end reinforcement learning), so that they can share algorithms. Autonomous driving practitioners excel at AI algorithms and quick iterations, but they need to learn knowledge about hardware interaction such as mechanical control (such as force feedback and motion planning), and face the challenges like uncertainty in technical routes and commercial verification. Their aims to dominate early EAI technology through collaborative innovation of software and hardware.
Robotics and automotive industries: collaborative development with similarities and differences
Industrial commonality: collaborative foundation of technology and supply chain
Hardware: The robotics and automotive industries involve highly similar hardware components. Motors, sensors, deceleration/conversion mechanisms, batteries, bearings, structural parts, cooling systems, controllers, chips and other hardware are widely used in both industries. For example, the design of Tesla Optimus borrows heavily from automotive hardware technology.
Sensors: LiDAR, cameras, radar, etc. all play key roles in both autonomous driving and robotic navigation. Vehicles use LiDAR to accurately detect obstacles ahead and recognize road boundaries to achieve autonomous driving. AI robots scan the surrounding environment by LiDAR to flexibly avoid obstacles and navigate accurately. In addition, automotive ECUs and robot motion controllers share the same underlying logic. They both receive sensor signals, quickly calculate according to the preset algorithms, and issue instructions to actuators.
Software technology: The algorithms accumulated by OEMs in the field of autonomous driving provide valuable experience for the development of EAI. In autonomous task processing, humanoid robots and autonomous vehicles follow the process of 'perception - decision-making - execution', and they are the same to a certain extent at the model level. Key algorithms for path planning and motion trajectory prediction, intelligent driving algorithms can be reused on humanoid robots.
Supply chain: The mature experience of the automotive supply chain provides strong support for the development of the robotics industry. The robotics industry chain and the automotive supply chain have the same technical origins in some parts and components, such as batteries, motors, bearings and so on. After long-term development, the automotive supply chain has experience in large-scale automated production and can help achieve mass production of robots with lower costs.
Layout: key considerations for OEMs and suppliers
Technology R&D and innovation
They should invest heavily in core technologies of EAI, such as foundation models, sensor fusion, motion control, human-computer interaction and other fields. For example, in the research and development of Optimus, Tesla has continuously optimized the effects of migrating FSD technology to enhance the robot's perception and decision-making capabilities; Huawei continues to iterate the Pangu Models to enhance task planning and multi-scenario generalization capabilities for EAI.
OEMs and suppliers should avoid duplicating research and development, as the automotive and robotics industries have significant commonalities in many aspects. In terms of supply chain, the raw materials and parts supply channels required by the two often overlap. Integrating supply chain resources can significantly reduce procurement costs and management difficulties. At the software technology level, AI foundation models and deep learning algorithms are the core driving forces for the realization of EAI. Sharing these technical achievements can accelerate the research and development process. As for hardware technology, sensors perceiving the environment as well as motors, gears, bearings, etc. which handle power transmission and mechanical movement are highly similar. Unifying technical standards and R&D plans can avoid repeated R&D investment.
Market demand and application scenario expansion
In the emerging EAI field, market demand and application scenarios are still being explored and expanded. Enterprises should delve in market surveys, gain an in-depth understanding of the demand of different industries and users, and develop targeted products and solutions. They should actively cooperate with potential customers, carry out pilot projects and application demonstrations, accumulate market experience, and improve the market adaptability and competitiveness of their products.
In addition to common scenarios such as industrial manufacturing, logistics warehousing, and home services, they should vigorously explore the application potential of EAI in sectors such as medical care, education and agriculture. For example, they should develop assistive robots for medical rehabilitation, intelligent robots for education, picking and farming robots for agricultural production, etc. By expanding application scenarios, they can seize more market share and depend less on a single market.
Talent training and introduction
EAI involves multiple disciplines, so OEMs and suppliers should recruit and train talents with interdisciplinary knowledge and skills. For example, Geely, BYD, Huawei and other companies established dedicated EAI research teams at the end of 2024, and they clearly require candidates to have knowledge in multiple fields such as machinery, automation, mechanics, computers, mathematics, electronic information and computing when recruiting talents. Companies can also improve the interdisciplinary capabilities of existing employees and build a professional talent team through internal training, cooperation with universities, etc.
Development Trends of EAI
- Technology Trends
- EAI Promotes the Development of Robotics Technology from Simple Perception to Multi-modal Perception
- Coordinated Development of Software and Hardware Promotes Positive Industry Cycle
- The Vision + Touch Solution Has Better Technical Indicators and Is Expected to Become the Mainstream Solution in the Future
- Technology Trends
- Product Trends
- Various Robot Carrier Forms Develop Together
- EAI and Various Robot Carrier Forms Develop Together - Robot Dog
- EAI and Various Robot Carrier Forms Develop Together - Special Robots
- Product Trends
- Industrial Market Trends
- Global and Chinese Humanoid Robot Markets Continue to Grow
- Market Demand Trend: Humanoid Robots Are Expected to Alleviate the Labor Shortage in the Market
- An Investment Boom in EAI in 2024 with Incremental Components and Embodied Models Favored by Investors
- Industry Trends
Layout of OEMs in EAI
- Layout of Global OEMs in EAI Robots
- Robot Deployment Timeline of OEMs
- Parameters of OEMs' Latest Robots
- Tesla
- Xpeng
- Xiaomi
- GAC
- BYD
- Hyundai
- Chery
- Toyota
- Huawei
EAI Layout of Automotive Industry Chain Suppliers
- Sensor
- RoboSense
- Tuopu Group
- Sanhua Intelligent Controls
- Inovance
- Zhaowei Machinery & Electronics
- Shuanghuan Company
- Thundersoft
- CATL
- ZongMu Technology
- FlashBot
- SenseTime
- Haomo.AI
- Horizon Robotics
For more information about this report visit https://www.researchandmarkets.com/r/v19r0p
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