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市場調查報告書
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2119285

機器人平台模型：市場佔有率分析、產業趨勢與統計及成長預測（2026-2031 年）

Robotics Foundation Models - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 - 2031)
出版日期: | 出版商: Mordor Intelligence | 英文 162 Pages | 商品交期: 2-3個工作天內

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簡介目錄

根據 Mordor Intelligence 預測，機器人平台模型的市場規模預計將從 2025 年的 9,746 萬美元和 2026 年的 1.4401 億美元成長到 2031 年的 7.8786 億美元，2026 年至 2031 年的複合年成長率為 40.48%。

機器人基金會模式-市場-IMG1

本報告按模型架構（視覺-語言-動作模型、具身推理模型等）、部署模式（雲端、本地部署）、應用領域（倉庫揀貨分揀、工業組裝作業等）、終端用戶行業（製造商、系統整合商等）和地區進行細分。市場預測以美元計價。

全球機器人基礎模型市場趨勢及洞察。

工業自動化和人手不足

隨著人手不足蔓延至一些難以用固定設備完成的任務，製造業和物流業的企業正在增加對自動化領域的投資。預計2025年，美國工業機器人的部署數量將成長11%，達到38,000台，機器人密度為每10,000名運作員工配備307台機器人。在此背景下，能夠解讀不斷變化的產品佈局、包裝形狀和作業指令的機器人具有顯著優勢。基礎型機器人可以輔助完成庫存補充、整合以及其他需要對傳統機器人進行大規模重新編程的操作。預計2025年，全球工業機器人的部署數量將達到621,000台，其中亞洲佔79%。這表明自動化浪潮正在向北美以外的地區蔓延。因此，隨著越來越多的雇主尋求靈活的功能，而非那些需要在本地運營、庫存水平或產品需求發生變化時進行重新配置的、功能單一的機器，基礎型機器人的市場將從中受益。

通用機器人智慧的需求

部署專用於特定任務的機器人的公司，在產品線或工廠佈局發生變化時，往往會面臨新的整合成本。這個問題促使人們對能夠跨任務和機器人類型遷移已學習技能的系統產生了濃厚的興趣。物理智慧公司報告稱，其π0.7模型能夠整合從不同資料集中學習到的技能，無需針對特定任務進行訓練即可完成新的操作序列。這一結果表明，通用機器人智慧具有實際提案，尤其是在汽車、電子和物流等高度可變的營運領域。實施更廣泛的機器人策略可以減少為每個搬運和組裝任務維護單獨系統的需求。這項機會正在推動機器人基礎模型市場的發展，因為在任務變更會增加整合負擔並延遲營運投資回報的工廠中，企業越來越將模型利用和持續更新視為其自動化基礎設施的核心要素。

真實世界機器人數據高成本且稀缺

在真實環境中示範機器人需要同步視覺、力量、運動和語言數據，但大規模商業化收集這些數據成本極為高。在服裝搬運、外科手術和複雜組裝等任務中，這項限制尤其突出，因為物理差異性限制了僅使用合成數據的價值。 2026 年，NVIDIA-Medtech 發布了“Open-H-Embodiment”，其中包含從 50 多家機構、20 個機器人平台收集的 770 小時手術機器人數據。如此龐大的協作式資料集表明，要實現跨機構的運動和資料流標準化，需要付出巨大的營運努力。 Universal Robots 和 Scale AI 共同推出了用於工業 VLA 訓練的“UR AI Trainer”，它可以從生產機器人中獲取同步的運動、力和視覺數據。雖然模擬、跨具身學習和共用資料集正在改善那些沒有大規模營運機器人或專門資料團隊的公司獲取資料的途徑，但擁有機器人車隊的公司仍然在機器人基礎模型市場佔據主導地位。

細分市場分析

到 2025 年，視覺、語言和動作 (VLA) 模型將佔據機器人基礎模型市場 54.67% 的佔有率。這項領先優勢源自於其能夠在單一主幹架構上處理影像、語言任務說明和機器人狀態資料的能力。這種設計無需感知、規劃和動作組件之間的複雜協調，即可實現跟隨方向的行為。 NVIDIA 將 GR00T N1.7 描述為一款開放的、商業授權的 VLA 模型，用於通用人形機器人技能，並已確定了包括 Unitree Robotics 和 Agile Robots 在內的培訓合作夥伴。憑藉成熟的工業部署經驗，VLA 模型在機器人基礎模型市場中擁有清晰的商業部署路徑，尤其適用於執行重複性搬運和組裝任務的成熟工業環境。

雖然世界模型所佔收入佔有率較小，但它們仍然十分重要，因為它們代表了用於規劃和測試的物理環境。 NVIDIA-Medtech 表示，Cosmos-H-Surgical-Simulator 可以利用九個手術平台上的機器人運動學數據來產生逼真的手術影片，從而支援部署前檢驗。預計到 2031 年，具身共振模型將以 46.53% 的複合年成長率成長，成為架構領域中成長最快的細分市場。 Physical Intelligence 報告稱，π0.7 在諸如煮咖啡、疊衣服和組裝盒子等任務中，無需特定任務的訓練數據，其性能就與特定任務系統相當。行為策略、跨具身控制和工具編配模型則滿足了更具體的需求。在機器人基礎模型市場，開發框架的進步使得這些模型更容易被需要經濟實惠的入門方案和高度適應性控制策略的小規模整合商所接受。

到2025年，基於雲端的部署將佔總收入的57.26%。企業正在利用雲端系統進行集中式模型更新、共用運算資源以及跨地域聚合機器人資料。 Universal Robots和Scale AI表示，UR AI Trainer可從生產車間收集運動、力和視覺數據，並支持從實驗室到工廠的模仿學習。該模型將部署數據與精細的控制策略相結合，並降低了中型企業的基礎設施門檻。預計到2031年，雲端通路將以43.61%的複合年成長率成長，成為機器人基礎模型市場中成長最快的部署模式。

在需要資料管理、低延遲或可靠本地運行的場景中，本地部署仍然至關重要。在國防設施、製藥無塵室和遠端採礦作業中，依賴外部雲端連接可能不可接受。 NVIDIA Jetson Thor 可實現邊緣即時機器人推理和控制，幫助相關設施維護敏感的運作資料。歐洲的資料居住法規和行業特定的安全要求也在推動雲端使用受限的本地部署。在機器人基礎模型市場，隨著客戶在模型改進、運行控制、本地容錯和資料保護義務之間尋求平衡，基於雲端的學習和邊緣控制的共存將繼續發揮重要作用。

區域分析

到2025年，北美將佔據機器人基礎模型市場45.74%的佔有率。該地區是尖端模型開發商、雲端基礎設施和企業自動化投資的聚集地。到2025年，美國部署的工業機器人數量將成長11%，達到38,000台，機器人密度為每10,000名製造業工人配備307台機器人，這將支援其在製造業、物流及相關服務業的部署。 ANSI/A3 R15.06-2025標準制定了符合ISO 10218標準的最新工業安全要求，為部署者提供了更清晰的檢驗標準。

預計到2031年，亞太地區將以46.84%的複合年成長率成長，成為成長最快的地區市場。根據日本機器人協會預測，2025年機器人訂單預計將達到1.0456兆日圓（約69.7億美元），年增25.7%，2026年將達到1.22兆日圓（約81.3億美元）。日本正透過「人工智慧機器人基礎技術聯盟」推動資料訂單和基礎模型共同開發。在韓國，每萬名製造業員工擁有1,220台機器人，為電子和半導體生產產業的現有設施維修提供了大規模部署基地。在亞太地區，機器人基礎模型的市場可以透過新安裝和現有自動化設備的升級來拓展。

到2024年，歐洲的機器人密度（每萬名製造業工人）將達到267台，根據國際機器人聯合會（IFR）報告，這一水準在所有地區中最高。雖然現有的自動化基礎設施正在推動機器人技術的應用，但與北美和亞太地區相比，歐洲複雜的合規要求和對尖端機型投資的放緩可能會限制其普及速度。南美洲、中東和非洲是機器人基礎機型市場的新興小規模市場。預計巴西的汽車組裝和食品加工產業將對機器人基礎機型產生需求，而南非則在礦場巡檢和危險環境導航等領域擁有重要的應用情境。海灣國家在機器人基礎機型市場的更廣泛應用將取決於雲端基礎設施、智慧製造和物流舉措，以及反映區域語言、工作環境和運作條件的在地化數據。

其他好處：

  • Excel格式的市場預測（ME）表
  • 3個月的分析師支持

目錄

第1章：引言

  • 研究假設和市場定義
  • 調查範圍

第2章：調查方法

第3章執行摘要

第4章 市場狀況

  • 市場概覽
  • 市場促進因素
    • 工業自動化和勞動力短缺
    • 通用機器人智慧化的需求日益成長
    • 擴展多模態視覺、語言和行為模型
    • 開放式機器人模式降低了研發門檻。
    • 透過商業部署實現機器人數據的飛輪效應
    • 透過以模擬為優先的訓練方式，減少所需的實體資料量。
  • 市場限制因素
    • 現實世界中機器人數據的高成本與稀缺性
    • 安全認證和問責的不確定性
    • 長尾任務中具身遷移的失敗
    • 推理經濟學和邊緣運算的約束
  • 產業價值鏈分析
  • 監理情勢
  • 技術展望
  • 波特五力分析

第5章 市場規模與成長預測

  • 模型架構
    • 視覺、語言和行動模型
    • 具體推理模型
    • 世界模型
    • 其他模型架構（行為策略模型、跨體現控制模型、工具編配模型）
  • 部署模式
    • 基於雲端的
    • 現場
  • 透過使用
    • 倉庫揀貨與分類
    • 工業組裝工作
    • 物料運輸包裝
    • 移動檢測和導航
    • 家庭服務實施
    • 其他用途（商業服務、醫療支援、農業和田間作業、國防和安全行動、危險環境作業、研究和開發）
  • 按最終用戶行業分類
    • 製造商
    • 物流/倉儲營運商
    • 系統整合商
    • 醫療服務提供方
    • 國防和安全機構
    • 其他終端用戶產業
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 墨西哥
    • 南美洲
      • 巴西
      • 阿根廷
      • 智利
      • 其他南美國家
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 義大利
      • 俄羅斯
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 韓國
      • 印度
      • 東南亞
      • 其他亞太國家
    • 中東
      • 土耳其
      • 以色列
      • 海灣合作理事會國家
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 其他非洲國家

第6章 競爭情勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • NVIDIA Corporation
    • Alphabet Inc.
    • Physical Intelligence
    • Skild AI
    • Covariant
    • Figure AI, Inc.
    • Field AI, Inc.
    • 1X Technologies AS
    • Boston Dynamics, Inc.
    • Microsoft Corporation
    • Amazon.com, Inc.
    • Agility Robotics, Inc.
    • Sanctuary Cognitive Systems Corporation
    • Apptronik, Inc.
    • Genesis AI
    • Dyna Robotics, Inc.
    • NEURA Robotics GmbH
    • Agile Robots SE
    • UBTECH Robotics Corp Ltd
    • Toyota Motor Corporation
    • Tesla, Inc.
    • Huawei Technologies Co., Ltd.
    • Tencent Holdings Limited
    • Agibot Inc.
    • Unitree Robotics

第7章 市場機會與未來展望

簡介目錄
Product Code: 101460

According to Mordor Intelligence, the robotics foundation models market size is projected to expand from USD 97.46 million in 2025 and USD 144.01 million in 2026 to USD 787.86 million by 2031, registering a CAGR of 40.48% between 2026 to 2031.

Robotics Foundation Models - Market - IMG1

This report is Segmented by Model Architecture (Vision-Language-Action Models, Embodied Reasoning Models, and More), Deployment Mode (Cloud-Based, and On-Premises), Application (Warehouse Picking and Sorting, Industrial Assembly Operations, and More), End-User Industry (Manufacturers, System Integrators, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Robotics Foundation Models Market Trends and Insights

Industrial Automation and Labor Shortages

Manufacturing and logistics employers are increasing automation investment because shortages now affect tasks that fixed equipment cannot easily perform. U.S. industrial robot installations rose 11% to 38,000 units in 2025, while robot density reached 307 operational units per 10,000 manufacturing employees. These conditions favor robots that can interpret changing product layouts, package shapes, and work instructions. Foundation models can support restocking, mixed-item handling, and other tasks where conventional robots require extensive reprogramming. Global industrial robot installations reached 621,000 units in 2025, and Asia accounted for 79% of installations, showing that the automation push extends beyond North America. The robotics foundation models market therefore benefits when employers seek flexible capacity rather than another narrowly programmed machine that must be reconfigured when local tasks, stock profiles, or product conditions change.

Demand for General-Purpose Robot Intelligence

Enterprises with task-specific robots often incur new integration costs when product lines or facility layouts change. This issue has increased interest in systems that can transfer learned skills across tasks and robot types. Physical Intelligence reported that its π0.7 model combined skills learned from separate datasets to complete novel manipulation sequences without task-specific training. The result points to a practical value proposition for general-purpose robot intelligence, particularly in automotive, electronics, and logistics operations with frequent variation. A broader robot policy may reduce the need to maintain separate systems for each handling or assembly task. This opportunity supports the robotics foundation models market because enterprises increasingly view model access and ongoing updates as core automation infrastructure for facilities where changing tasks otherwise increase integration effort and delay operational returns.

High Cost and Scarcity of Real-World Robot Data

Real-world robot demonstrations require synchronized visual, force, motion, and language data, which is costly to collect at a commercial scale. The constraint is most evident in garment handling, surgical work, and complex assembly, where physical variation limits the value of synthetic data alone. NVIDIA-Medtech released Open-H-Embodiment in 2026 with 770 hours of surgical robot data from 50 or more institutions across 20 robot platforms. The scale of this coordinated dataset also shows the operational work needed to standardize actions and data streams across institutions. Universal Robots and Scale AI introduced UR AI Trainer to capture synchronized motion, force, and vision data from production robots for industrial VLA training. The robotics foundation models market remains advantaged toward companies with deployed fleets, even as simulation, cross-embodiment training, and shared datasets improve access for firms without large operating fleets or specialist data teams.

Other drivers and restraints analyzed in the detailed report include:

  1. Expansion of Multimodal Vision-Language-Action Models
  2. Open Robotics Models Lowering Development Barriers
  3. Safety Certification and Liability Uncertainty

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Vision-Language-Action models held 54.67% of the robotics foundation models market share in 2025. Their lead reflects the ability to process images, language task descriptions, and robot-state data in one backbone. This design supports instruction-following behavior without complex links between separate perception, planning, and action components. NVIDIA described GR00T N1.7 as an open, commercially licensed VLA for general humanoid robot skills and identified training partners, including Unitree Robotics and Agile Robots. The industrial installed base gives VLA models a clear commercial route across established industrial settings with repeatable handling and assembly work in the robotics foundation models market.

World models hold a smaller revenue position but remain important because they represent physical environments for planning and testing. NVIDIA-Medtech stated that Cosmos-H-Surgical-Simulator generated realistic surgical video from robot kinematics across nine surgical platforms, which can support validation before physical deployment. Embodied reasoning models are projected to expand at a 46.53% CAGR through 2031, the fastest rate within the architecture segment. Physical Intelligence reported that π0.7 matched task-specific systems on coffee preparation, laundry folding, and box assembly without task-specific training data. Behavior policy, cross-embodiment control, and tool-orchestration models serve narrower needs as development frameworks improve access for smaller integrators that need affordable starting points and adaptable control policies in the robotics foundation models market.

Cloud-based deployment accounted for 57.26% of revenue in 2025. Enterprises use cloud systems for centralized model updates, shared compute, and the aggregation of robot data across sites. Universal Robots and Scale AI stated that UR AI Trainer captures production motion, force, and vision data to support imitation learning from the lab to the factory. This model connects deployment data with policy refinement and lowers infrastructure barriers for mid-sized enterprises. The cloud channel is projected to expand at a 43.61% CAGR through 2031, making it the fastest deployment mode in the robotics foundation models market.

On-premises deployment remains important where operations need data control, low latency, or reliable local operation. Defense sites, pharmaceutical cleanrooms, and remote mining operations may not accept dependence on external cloud connections. NVIDIA positioned Jetson Thor for real-time robot inference and control at the edge, helping facilities retain sensitive operational data. European data-residency rules and sector security requirements also support local deployment where cloud use is restricted. The coexistence of cloud learning and edge control will remain relevant as customers balance model improvement with operational control, local resilience, and data-protection obligations in the robotics foundation models market.

Complete Report Scope:

  • By Model Architecture
    • Vision-Language-Action Models
    • Embodied Reasoning Models
    • World Models
    • Other Model Architectures (Behavior Policy Models, Cross-Embodiment Control Models, Tool-Orchestration Models)
  • By Deployment Mode
    • Cloud-Based
    • On-Premises
  • By Application
    • Warehouse Picking and Sorting
    • Industrial Assembly Operations
    • Material Handling and Packaging
    • Mobile Inspection and Navigation
    • Home Service Execution
    • Other Applications (Commercial Service Interaction, Medical Care Assistance, Agriculture and Field Operations, Defense and Security Operations, Hazardous-Environment Operations, Research and Education Development)
  • By End-User Industry
    • Manufacturers
    • Logistics and Warehousing Providers
    • System Integrators
    • Healthcare Providers
    • Defense and Security Organizations
    • Other End-User Industries
  • By Geography
    • North America
      • United States
      • Canada
      • Mexico
    • South America
      • Brazil
      • Argentina
      • Chile
      • Rest of South America
    • Europe
      • Germany
      • United Kingdom
      • France
      • Italy
      • Russia
      • Rest of Europe
    • Asia-Pacific
      • China
      • Japan
      • South Korea
      • India
      • Southeast Asia
      • Rest of Asia-Pacific
    • Middle East
      • Turkey
      • Israel
      • GCC Countries
      • Rest of Middle East
    • Africa
      • South Africa
      • Egypt
      • Rest of Africa

Geography Analysis

North America held 45.74% of the robotics foundation models market share in 2025. The region combines frontier model developers, cloud infrastructure, and enterprise spending on automation. U.S. industrial robot installations increased by 11% to 38,000 units in 2025, while robot density reached 307 units per 10,000 manufacturing employees, supporting deployments in manufacturing, logistics, and related services. ANSI/A3 R15.06-2025 sets updated industrial safety requirements that align with ISO 10218 and create a clearer validation reference for deployers.

Asia-Pacific is projected to expand at a 46.84% CAGR through 2031, representing the fastest-growing regional opportunity. The Japan Robot Industry Association reported that 2025 robot orders rose 25.7% to JPY 1,045.6 billion (USD 6.97 billion), and forecast 2026 orders of JPY 1,220 billion (USD 8.13 billion). Japan is coordinating data collection and shared development of foundation models through the AI Robot Foundation Technology Consortium. South Korea had a robot density of 1,220 units per 10,000 manufacturing employees, creating a large installed base for retrofits in electronics and semiconductor production. Asia-Pacific can expand the robotics foundation models market through new deployments and upgrades to established automated facilities.

Europe had a robot density of 267 units per 10,000 manufacturing employees in 2024, the highest regional level reported by the International Federation of Robotics. This installed automation base supports adoption, although detailed compliance requirements and slower investment in frontier models may limit the pace relative to North America and Asia-Pacific. South America, the Middle East, and Africa held a modest but emerging position in the robotics foundation models market. Brazil offers demand from automotive assembly and food processing, while South Africa offers a relevant use case in mining inspection and hazardous navigation. Broader adoption will depend on cloud infrastructure, smart manufacturing, and logistics initiatives in Gulf countries, as well as localized data that reflects regional languages, work environments, and operating conditions in the robotics foundation models market.

  1. NVIDIA Corporation
  2. Alphabet Inc.
  3. Physical Intelligence
  4. Skild AI
  5. Covariant
  6. Figure AI, Inc.
  7. Field AI, Inc.
  8. 1X Technologies AS
  9. Boston Dynamics, Inc.
  10. Microsoft Corporation
  11. Amazon.com, Inc.
  12. Agility Robotics, Inc.
  13. Sanctuary Cognitive Systems Corporation
  14. Apptronik, Inc.
  15. Genesis AI
  16. Dyna Robotics, Inc.
  17. NEURA Robotics GmbH
  18. Agile Robots SE
  19. UBTECH Robotics Corp Ltd
  20. Toyota Motor Corporation
  21. Tesla, Inc.
  22. Huawei Technologies Co., Ltd.
  23. Tencent Holdings Limited
  24. Agibot Inc.
  25. Unitree Robotics

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

  • 1.1 Study Assumptions and Market Definition
  • 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Industrial Automation and Labor Shortages
    • 4.2.2 Growing Demand for General-Purpose Robot Intelligence
    • 4.2.3 Expansion of Multimodal Vision-Language-Action Models
    • 4.2.4 Open Robotics Models Lowering Development Barriers
    • 4.2.5 Robot Data Flywheels From Commercial Deployments
    • 4.2.6 Simulation-First Training Reducing Physical Data Requirements
  • 4.3 Market Restraints
    • 4.3.1 High Cost and Scarcity of Real-World Robot Data
    • 4.3.2 Safety Certification and Liability Uncertainty
    • 4.3.3 Embodiment Transfer Failures in Long-Tail Tasks
    • 4.3.4 Inference Economics and Edge-Compute Constraints
  • 4.4 Industry Value-Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces Analysis
    • 4.7.1 Intensity of Competitive Rivalry
    • 4.7.2 Bargaining Power of Suppliers
    • 4.7.3 Bargaining Power of Buyers
    • 4.7.4 Threat of New Entrants
    • 4.7.5 Threat of Substitutes

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

  • 5.1 By Model Architecture
    • 5.1.1 Vision-Language-Action Models
    • 5.1.2 Embodied Reasoning Models
    • 5.1.3 World Models
    • 5.1.4 Other Model Architectures (Behavior Policy Models, Cross-Embodiment Control Models, Tool-Orchestration Models)
  • 5.2 By Deployment Mode
    • 5.2.1 Cloud-Based
    • 5.2.2 On-Premises
  • 5.3 By Application
    • 5.3.1 Warehouse Picking and Sorting
    • 5.3.2 Industrial Assembly Operations
    • 5.3.3 Material Handling and Packaging
    • 5.3.4 Mobile Inspection and Navigation
    • 5.3.5 Home Service Execution
    • 5.3.6 Other Applications (Commercial Service Interaction, Medical Care Assistance, Agriculture and Field Operations, Defense and Security Operations, Hazardous-Environment Operations, Research and Education Development)
  • 5.4 By End-User Industry
    • 5.4.1 Manufacturers
    • 5.4.2 Logistics and Warehousing Providers
    • 5.4.3 System Integrators
    • 5.4.4 Healthcare Providers
    • 5.4.5 Defense and Security Organizations
    • 5.4.6 Other End-User Industries
  • 5.5 By Geography
    • 5.5.1 North America
      • 5.5.1.1 United States
      • 5.5.1.2 Canada
      • 5.5.1.3 Mexico
    • 5.5.2 South America
      • 5.5.2.1 Brazil
      • 5.5.2.2 Argentina
      • 5.5.2.3 Chile
      • 5.5.2.4 Rest of South America
    • 5.5.3 Europe
      • 5.5.3.1 Germany
      • 5.5.3.2 United Kingdom
      • 5.5.3.3 France
      • 5.5.3.4 Italy
      • 5.5.3.5 Russia
      • 5.5.3.6 Rest of Europe
    • 5.5.4 Asia-Pacific
      • 5.5.4.1 China
      • 5.5.4.2 Japan
      • 5.5.4.3 South Korea
      • 5.5.4.4 India
      • 5.5.4.5 Southeast Asia
      • 5.5.4.6 Rest of Asia-Pacific
    • 5.5.5 Middle East
      • 5.5.5.1 Turkey
      • 5.5.5.2 Israel
      • 5.5.5.3 GCC Countries
      • 5.5.5.4 Rest of Middle East
    • 5.5.6 Africa
      • 5.5.6.1 South Africa
      • 5.5.6.2 Egypt
      • 5.5.6.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
    • 6.4.1 NVIDIA Corporation
    • 6.4.2 Alphabet Inc.
    • 6.4.3 Physical Intelligence
    • 6.4.4 Skild AI
    • 6.4.5 Covariant
    • 6.4.6 Figure AI, Inc.
    • 6.4.7 Field AI, Inc.
    • 6.4.8 1X Technologies AS
    • 6.4.9 Boston Dynamics, Inc.
    • 6.4.10 Microsoft Corporation
    • 6.4.11 Amazon.com, Inc.
    • 6.4.12 Agility Robotics, Inc.
    • 6.4.13 Sanctuary Cognitive Systems Corporation
    • 6.4.14 Apptronik, Inc.
    • 6.4.15 Genesis AI
    • 6.4.16 Dyna Robotics, Inc.
    • 6.4.17 NEURA Robotics GmbH
    • 6.4.18 Agile Robots SE
    • 6.4.19 UBTECH Robotics Corp Ltd
    • 6.4.20 Toyota Motor Corporation
    • 6.4.21 Tesla, Inc.
    • 6.4.22 Huawei Technologies Co., Ltd.
    • 6.4.23 Tencent Holdings Limited
    • 6.4.24 Agibot Inc.
    • 6.4.25 Unitree Robotics

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

  • 7.1 White-Space and Unmet-Need Assessment