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市場調查報告書
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2080157

全球實體人工智慧市場：按組件、載體、技術、自主等級、應用和最終用戶分類－市場規模、產業動態、機會分析和預測（2026-2035 年）

Global Physical AI Market: By Component, Embodiment, Technology, Autonomy Level, Application, End User - Market Size, Industry Dynamics, Opportunity Analysis and Forecast For 2026-2035
出版日期: | 出版商: Astute Analytica | 英文 240 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

專注於將人工智慧整合到現實世界有形系統中的實體人工智慧市場，例如機器人、自動駕駛汽車、工業機械和智慧基礎設施，正經歷著快速且變革性的成長。儘管該市場規模在2025年估計約為35億美元，但預計到2035年將顯著成長至約581億美元。這意味著在2026年至2035年的預測期內，該市場將以約32.4%的複合年成長率成長，凸顯了全球向智慧化和自動化實體系統加速轉型的趨勢。

這一顯著成長軌跡主要源自於各行業對自動化日益成長的需求，尤其是在那些高度依賴實體操作的行業。企業正不斷尋求能夠提高生產力、降低營運成本並最大限度減少人工干預的解決方案。實體人工智慧系統憑藉其將先進的機器學習演算法與感測器、執行器和機器人相結合的獨特優勢，能夠滿足這些需求，使機器能夠即時感知、解讀物理世界並與之互動。

顯著的市場趨勢

實體人工智慧市場日益被少數幾家關鍵企業所主導，這些企業掌控著技術堆疊的關鍵層面，從基礎設施和訓練系統到實體機器人和高階認知模型。 NVIDIA 是實體人工智慧基礎架構層面的絕對領導者，提供訓練、模擬和即時推理所需的運算能力。

特斯拉憑藉其大規模真實世界數據優勢和垂直整合的自動駕駛模式，在具身人工智慧領域佔主導地位。波士頓動力公司則持續引領先進機器人移動性和動態物理智慧的潮流。該公司以其高度靈活的機器人系統而聞名，為複雜和非結構化環境中的運動、平衡和環境互動樹立了行業標竿。

Figure AI 已成為開發具有商業性可行性的通用人形機器人領域的有力競爭者。該公司專注於建立高度擴充性的人形系統，以整合到工業、物流和服務環境中。 Google DeepMind 在實體人工智慧的認知和推理層面發揮主導作用，致力於開發先進的模型，使機器能夠在複雜環境中理解、規劃和行動。

主要成長促進因素

全球勞動力嚴重短缺正成為推動實體人工智慧市場需求的最強勁的結構性促進因素之一。在主要經濟體中，光是製造業目前就面臨超過800萬名工人的缺口，造成持續的營運瓶頸，而傳統的招募和培訓體系越來越難以解決這個問題。這種不斷擴大的勞動力短缺並非暫時性的或週期性的，而是反映了更深層次的人口結構變化、已開發地區勞動力老齡化以及年輕一代對體力勞動密集型工業工作興趣的下降。這種持續的短缺正在從根本上改變各行業的生產和服務交付方式。

新機會的趨勢

全球工業機器人部署量已正式突破450萬台，標誌著實體人工智慧技術正以運作中的速度推動製造業轉型。這一里程碑反映了主要工業國家工廠環境中機器人和智慧自動化技術的加速整合。機器人系統不再侷限於孤立的自動化單元或實驗性部署，而是深度融入核心生產流程，大規模重塑產品的設計、組裝和交付方式。在這個不斷擴展的生態系統中，電子製造業是物理人工智慧驅動的機器人技術最積極的應用產業之一。

最佳化障礙

儘管技術快速發展，「具身性」和「現實差距」仍是限制實體人工智慧市場成長的重大挑戰。人工智慧系統在受控的實驗室環境、模擬平台和高度結構化的測試條件下通常表現出色，但部署到真實環境時，其可靠性往往會下降。這種模擬效能與實際效果之間的差異，一直是推動具身人工智慧解決方案在整個產業中規模化應用的一大障礙。在受控環境中，由於光照、物件位置、運動模式和環境噪音等變數都嚴格控制，人工智慧模型和機器人系統能夠實現高精度和高一致性。

目錄

第1章摘要整理：全球實體人工智慧市場

第2章：調查方法與研究框架

  • 研究目標
  • 產品概述
  • 市場區隔
  • 定性研究
    • 一手和二手資訊
  • 量化研究
    • 一手和二手資訊
  • 主要調查受訪者組成：按地區分類
  • 本研究的前提
  • 市場規模估算
  • 數據三角測量

第3章：全球實體人工智慧市場概覽

  • 產業價值鏈分析
  • 產業展望
    • 全球實體人工智慧和嵌入式智慧產業概覽
    • 從視覺、語言和行動模型及模擬向現實世界的過渡
    • 人手不足、人形機器人的商業化以及安全性和自主性的限制。
  • PESTLE分析
  • 波特五力分析
  • 市場成長及前景
    • 2020-2035年市場收入估算與預測
    • 價格趨勢分析：按組件

第4章：全球實體人工智慧市場分析

  • 競爭對手儀表板
    • 市場集中度
    • 企業市場占有率分析，2025 年
    • 競爭對手分析與基準測試

第5章：全球實體人工智慧市場分析

  • 市場動態和趨勢
    • 生長促進因子
    • 抑制因子
    • 機會
    • 主要趨勢
  • 市場規模及預測（2020-2035）
    • 按組件
      • 關鍵見解
        • 軟體和基礎模型
          • 視覺-語言-行為模型
          • 政策/控制
        • 仿真和綜合環境
        • 機載計算硬體
        • 服務
    • 透過實施例
      • 關鍵見解
        • 人形機器人
        • 移動機器人/AMMR
        • 自動駕駛汽車
        • 工業機械手
        • 無人機
    • 透過技術
      • 關鍵見解
        • 視覺、語言、行動
        • 強化學習
        • 世界模型
        • 感測器融合
        • 模仿學習
    • 自主等級
      • 關鍵見解
        • 支援類型/遙控類型
        • 半自動自主
        • 完全自主
    • 用途別
      • 關鍵見解
        • 製造/物流
        • 流動性
        • 醫療保健服務
        • 農業
        • 防禦
    • 最終用戶
      • 關鍵見解
        • 產業
        • 商業的
        • 車
        • 防禦
        • 調查
    • 按地區
      • 關鍵見解
        • 北美洲
          • 美國
          • 加拿大
          • 墨西哥
        • 歐洲
          • 西歐
            • 英國
            • 德國
            • 法國
            • 義大利
            • 西班牙
            • 其他西歐國家
          • 東歐
            • 波蘭
            • 俄羅斯
            • 其他歐洲國家
        • 亞太地區
          • 中國
          • 印度
          • 日本
          • 澳洲和紐西蘭
          • 韓國
          • ASEAN
          • 亞太其他地區
        • 中東和非洲（MEA）
          • 沙烏地阿拉伯
          • 南非
          • 阿拉伯聯合大公國
          • 其他中東和非洲地區
        • 南美洲
          • 阿根廷
          • 巴西
          • 南美洲其他地區

第6章：北美市場分析

第7章：歐洲市場分析

第8章：亞太市場分析

第9章：中東和非洲市場分析

第10章：南美市場分析

第11章：公司簡介

  • Cera
  • Cleerly
  • CMR Surgical
  • Diligent Robotics
  • Ekso Bionics
  • Intuitive Surgical
  • Medtronic
  • NDR Medical Technology
  • Owkin
  • PathAI
  • SWORD Health
  • Tempus
  • 其他主要公司

第12章附錄

簡介目錄
Product Code: AA06261842

The Physical AI market, which focuses on integrating artificial intelligence into real-world, tangible systems such as robotics, autonomous vehicles, industrial machinery, and smart infrastructure, is witnessing rapid and transformative expansion. In 2025, the market is estimated to be valued at approximately USD 3.5 billion, but it is projected to grow significantly to around USD 58.1 billion by 2035. This represents a strong compound annual growth rate (CAGR) of about 32.4% during the forecast period from 2026 to 2035, highlighting the accelerating global shift toward intelligent, automated physical systems.

This substantial growth trajectory is being driven primarily by the rising demand for automation across industries that rely heavily on physical operations. Enterprises are increasingly seeking solutions that can enhance productivity, reduce operational costs, and minimize dependency on manual labor. Physical AI systems are uniquely positioned to address these needs by combining advanced machine learning algorithms with sensors, actuators, and robotics to enable machines to perceive, interpret, and interact with the physical world in real time.

Noteworthy Market Developments

The physical AI market is increasingly shaped by a small group of dominant players that collectively control critical layers of the technology stack, ranging from infrastructure and training systems to embodied robotics and advanced cognitive models. NVIDIA stands as the undisputed leader in the infrastructure layer of physical AI, providing the computational backbone required for training, simulation, and real-time inference.

Tesla dominates the embodied AI space through its large-scale real-world data advantage and vertically integrated approach to autonomy. Boston Dynamics remains the benchmark for advanced robotic mobility and dynamic physical intelligence. Known for its highly agile robotic systems, the company has set the standard for movement, balance, and environmental interaction in complex, unstructured settings.

Figure AI has emerged as a leading contender in the race to develop commercially viable general-purpose humanoid robots. The company is focused on building scalable humanoid systems designed for integration into industrial, logistics, and service environments. Google DeepMind plays a dominant role in the cognitive and reasoning layer of physical AI, focusing on the development of advanced models that enable machines to understand, plan, and act in complex environments.

Core Growth Drivers

Severe global labor shortages are emerging as one of the most powerful structural forces driving demand in the physical AI market. Across major economies, the manufacturing sector alone is currently facing a shortfall of more than 8 million workers, creating persistent operational bottlenecks that are increasingly difficult to resolve through traditional hiring or training pipelines. This widening labor gap is not temporary or cyclical; it reflects deeper demographic shifts, aging workforces in developed regions, and declining interest in physically demanding industrial roles among younger populations. This sustained deficit is fundamentally reshaping how industries approach production and service delivery.

Emerging Opportunity Trends

The global installed base of industrial robots has now formally surpassed 4.5 million active units, signaling a profound transformation in manufacturing operations driven by physical AI technologies. This milestone reflects the accelerating integration of robotics and intelligent automation into factory environments across major industrial economies. Rather than being limited to isolated automation cells or experimental deployments, robotic systems are now deeply embedded into core production workflows, reshaping how goods are designed, assembled, and delivered at scale. Within this expanding ecosystem, the electronics manufacturing sector represents one of the largest adopters of physical AI-driven robotics.

Barriers to Optimization

The Embodiment and "Reality Gap" represent a significant challenge that may constrain the growth of the physical AI market, despite rapid technological advancements. While AI systems demonstrate exceptional performance in controlled laboratory environments, simulation platforms, and highly structured test conditions, their reliability often diminishes when deployed in real-world settings. This disconnect between simulated performance and real-world effectiveness creates a persistent barrier to scaling embodied AI solutions across industries. In controlled environments, variables such as lighting, object positioning, movement patterns, and environmental noise are carefully regulated, allowing AI models and robotic systems to achieve high levels of accuracy and consistency.

Detailed Market Segmentation

By embodiment, humanoid robots account for a leading 42% share of the physical AI market in 2025, reflecting their rapid emergence as the most commercially versatile form factor in embodied intelligence. Their growing dominance highlights a major shift in how enterprises evaluate automation technologies, moving away from highly specialized, task-specific machines toward general-purpose robotic systems capable of operating across a wide range of environments and workflows. This transition is being driven by the increasing need for flexible automation solutions that can adapt to existing infrastructure without requiring extensive redesign or capital-intensive modifications.

By technology, Vision-Language-Action (VLA) models hold a dominant position in the physical AI market, accounting for approximately 55% of the total share. This leadership reflects their emergence as the core cognitive architecture enabling next-generation robotics and embodied intelligence systems. VLA models are increasingly viewed as foundational because they unify perception, reasoning, and action into a single integrated framework, allowing machines to interpret their environment, understand human intent, and execute physical tasks with greater adaptability than earlier generations of robotics systems.

By autonomy level, semi-autonomous systems account for a dominant 52% share of the physical AI market in 2025, establishing themselves as the primary operational standard across most commercial and industrial deployments. This leadership reflects a pragmatic balance between automation efficiency and human oversight, where AI systems are capable of performing complex tasks independently but still rely on human intervention for supervision, exception handling, or decision validation. In many enterprise environments, this hybrid model is considered the most viable pathway for integrating advanced robotics and AI-driven automation without compromising safety, reliability, or operational control.

By application, the Manufacturing and Logistics segment commands a substantial 38% share of the AI-driven automation market, establishing itself as the primary commercial proving ground for physical artificial intelligence systems. This dominance reflects the sector's rapid transformation as enterprises increasingly adopt AI-enabled robotics, autonomous systems, and intelligent automation to enhance productivity, reduce operational costs, and improve supply chain efficiency. Manufacturing and logistics environments provide ideal conditions for large-scale AI deployment due to their structured workflows, repetitive processes, and high-volume operational demands, making them a natural fit for industrial-grade automation technologies.

Segment Breakdown

By Component

  • Software & Foundation Models
  • VLA Models
  • Policy / Control
  • Simulation & Synthetic Env
  • Onboard Compute & Hardware
  • Services

By Embodiment

  • Humanoid Robots
  • Mobile Robots / AMRs
  • Autonomous Vehicles
  • Industrial Manipulators
  • Drones

By Technology

  • Vision-Language-Action
  • Reinforcement Learning
  • World Models
  • Sensor Fusion
  • Imitation Learning

By Autonomy Level

  • Assisted / Teleoperated
  • Semi-Autonomous
  • Fully Autonomous

By Application

  • Manufacturing & Logistics
  • Mobility
  • Healthcare & Service
  • Agriculture
  • Defense

By End User

  • Industrial
  • Commercial
  • Automotive
  • Defense
  • Research

By Region

  • North America
  • The U.S.
  • Canada
  • Mexico
  • Europe
  • Western Europe
  • The UK
  • Germany
  • France
  • Italy
  • Spain
  • Rest of Western Europe
  • Eastern Europe
  • Poland
  • Russia
  • Rest of Eastern Europe
  • Asia Pacific
  • China
  • India
  • Japan
  • Australia & New Zealand
  • South Korea
  • ASEAN
  • Rest of Asia Pacific
  • Middle East & Africa (MEA)
  • Saudi Arabia
  • South Africa
  • UAE
  • Rest of MEA
  • South America
  • Argentina
  • Brazil
  • Rest of South America

Geography Breakdown

  • North America continues to firmly dominate the global AI market, securing an estimated 48% share in 2026 and reinforcing its position as the primary hub for advanced artificial intelligence innovation and deployment. This leadership is underpinned by the region's concentration of world-leading research institutions, technology corporations, and AI infrastructure providers, which collectively drive both foundational model development and large-scale commercial applications.
  • A key factor reinforcing North America's dominance is the presence of influential technology leaders and ecosystem-defining companies that span both hardware and software layers of the AI stack. Organizations such as Nvidia play a central role in powering AI compute infrastructure through advanced GPU architectures, while Tesla contributes significantly to real-world AI deployment in autonomous systems and robotics-driven automation.
  • The region also benefits from exceptionally strong venture capital activity, particularly in sectors focused on embodied AI, robotics, and next-generation automation systems. Substantial funding flows into startups and scale-ups, enabling rapid prototyping, iterative testing, and accelerated product development cycles. This financial ecosystem allows companies to move from early-stage concepts to real-world deployment in significantly shorter timeframes compared to other regions.

Leading Market Participants

  • Cera
  • Cleerly
  • CMR Surgical
  • Diligent Robotics
  • Ekso Bionics
  • Intuitive Surgical
  • Medtronic
  • NDR Medical Technology
  • Owkin
  • PathAI
  • SWORD Health
  • Tempus
  • Other Prominent Players

Table of Content

Chapter 1. Executive Summary: Global Physical AI Market

Chapter 2. Research Methodology & Research Framework

  • 2.1. Research Objective
  • 2.2. Product Overview
  • 2.3. Market Segmentation
  • 2.4. Qualitative Research
    • 2.4.1. Primary & Secondary Sources
  • 2.5. Quantitative Research
    • 2.5.1. Primary & Secondary Sources
  • 2.6. Breakdown of Primary Research Respondents, By Region
  • 2.7. Assumption for Study
  • 2.8. Market Size Estimation
  • 2.9. Data Triangulation

Chapter 3. Global Physical AI Market Overview

  • 3.1. Industry Value Chain Analysis
    • 3.1.1. AI Compute, Chips & Edge Hardware Providers
    • 3.1.2. Foundation Model (VLA) & Robotics Software Developers
    • 3.1.3. Simulation & Synthetic-Data Platform Providers
    • 3.1.4. Robot / Embodiment OEMs & System Integrators
    • 3.1.5. End Users (Industrial, Automotive, Healthcare, Defense)
  • 3.2. Industry Outlook
    • 3.2.1. Overview of the Global Physical AI & Embodied-Intelligence Industry
    • 3.2.2. Vision-Language-Action Models and Simulation-to-Real Transfer
    • 3.2.3. Labor Shortages, Humanoid Commercialization & Safety / Autonomy Constraints
  • 3.3. PESTLE Analysis
  • 3.4. Porter's Five Forces Analysis
    • 3.4.1. Bargaining Power of Suppliers
    • 3.4.2. Bargaining Power of Buyers
    • 3.4.3. Threat of Substitutes
    • 3.4.4. Threat of New Entrants
    • 3.4.5. Degree of Competition
  • 3.5. Market Growth and Outlook
    • 3.5.1. Market Revenue Estimates and Forecast (US$ Mn), 2020-2035
    • 3.5.2. Price Trend Analysis, By Component

Chapter 4. Global Physical AI Market Analysis

  • 4.1. Competition Dashboard
    • 4.1.1. Market Concentration Rate
    • 4.1.2. Company Market Share Analysis (Value %), 2025
    • 4.1.3. Competitor Mapping & Benchmarking

Chapter 5. Global Physical AI Market Analysis

  • 5.1. Market Dynamics and Trends
    • 5.1.1. Growth Drivers
    • 5.1.2. Restraints
    • 5.1.3. Opportunity
    • 5.1.4. Key Trends
  • 5.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 5.2.1. By Component
      • 5.2.1.1. Key Insights
        • 5.2.1.1.1. Software & Foundation Models
          • 5.2.1.1.1.1. Vision-Language-Action Models
          • 5.2.1.1.1.2. Policy / Control
        • 5.2.1.1.2. Simulation & Synthetic Environments
        • 5.2.1.1.3. Onboard Compute & Hardware
        • 5.2.1.1.4. Services
    • 5.2.2. By Embodiment
      • 5.2.2.1. Key Insights
        • 5.2.2.1.1. Humanoid Robots
        • 5.2.2.1.2. Mobile Robots / AMRs
        • 5.2.2.1.3. Autonomous Vehicles
        • 5.2.2.1.4. Industrial Manipulators
        • 5.2.2.1.5. Drones
    • 5.2.3. By Technology
      • 5.2.3.1. Key Insights
        • 5.2.3.1.1. Vision-Language-Action
        • 5.2.3.1.2. Reinforcement Learning
        • 5.2.3.1.3. World Models
        • 5.2.3.1.4. Sensor Fusion
        • 5.2.3.1.5. Imitation Learning
    • 5.2.4. By Autonomy Level
      • 5.2.4.1. Key Insights
        • 5.2.4.1.1. Assisted / Teleoperated
        • 5.2.4.1.2. Semi-Autonomous
        • 5.2.4.1.3. Fully Autonomous
    • 5.2.5. By Application
      • 5.2.5.1. Key Insights
        • 5.2.5.1.1. Manufacturing & Logistics
        • 5.2.5.1.2. Mobility
        • 5.2.5.1.3. Healthcare & Service
        • 5.2.5.1.4. Agriculture
        • 5.2.5.1.5. Defense
    • 5.2.6. By End User
      • 5.2.6.1. Key Insights
        • 5.2.6.1.1. Industrial
        • 5.2.6.1.2. Commercial
        • 5.2.6.1.3. Automotive
        • 5.2.6.1.4. Defense
        • 5.2.6.1.5. Research
    • 5.2.7. By Region
      • 5.2.7.1. Key Insights
        • 5.2.7.1.1. North America
          • 5.2.7.1.1.1. The U.S.
          • 5.2.7.1.1.2. Canada
          • 5.2.7.1.1.3. Mexico
        • 5.2.7.1.2. Europe
          • 5.2.7.1.2.1. Western Europe
            • 5.2.7.1.2.1.1. The UK
            • 5.2.7.1.2.1.2. Germany
            • 5.2.7.1.2.1.3. France
            • 5.2.7.1.2.1.4. Italy
            • 5.2.7.1.2.1.5. Spain
            • 5.2.7.1.2.1.6. Rest of Western Europe
          • 5.2.7.1.2.2. Eastern Europe
            • 5.2.7.1.2.2.1. Poland
            • 5.2.7.1.2.2.2. Russia
            • 5.2.7.1.2.2.3. Rest of Eastern Europe
        • 5.2.7.1.3. Asia Pacific
          • 5.2.7.1.3.1. China
          • 5.2.7.1.3.2. India
          • 5.2.7.1.3.3. Japan
          • 5.2.7.1.3.4. Australia & New Zealand
          • 5.2.7.1.3.5. South Korea
          • 5.2.7.1.3.6. ASEAN
          • 5.2.7.1.3.7. Rest of Asia Pacific
        • 5.2.7.1.4. Middle East & Africa (MEA)
          • 5.2.7.1.4.1. Saudi Arabia
          • 5.2.7.1.4.2. South Africa
          • 5.2.7.1.4.3. UAE
          • 5.2.7.1.4.4. Rest of MEA
        • 5.2.7.1.5. South America
          • 5.2.7.1.5.1. Argentina
          • 5.2.7.1.5.2. Brazil
          • 5.2.7.1.5.3. Rest of South America

Chapter 6. North America Market Analysis

  • 6.1. Market Dynamics and Trends
    • 6.1.1. Growth Drivers
    • 6.1.2. Restraints
    • 6.1.3. Opportunity
    • 6.1.4. Key Trends
  • 6.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 6.2.1. Key Insights
      • 6.2.1.1. By Component
      • 6.2.1.2. By Embodiment
      • 6.2.1.3. By Technology
      • 6.2.1.4. By Autonomy Level
      • 6.2.1.5. By Application
      • 6.2.1.6. By End User
      • 6.2.1.7. By Country

Chapter 7. Europe Market Analysis

  • 7.1. Market Dynamics and Trends
    • 7.1.1. Growth Drivers
    • 7.1.2. Restraints
    • 7.1.3. Opportunity
    • 7.1.4. Key Trends
  • 7.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 7.2.1. Key Insights
      • 7.2.1.1. By Component
      • 7.2.1.2. By Embodiment
      • 7.2.1.3. By Technology
      • 7.2.1.4. By Autonomy Level
      • 7.2.1.5. By Application
      • 7.2.1.6. By End User
      • 7.2.1.7. By Country

Chapter 8. Asia Pacific Market Analysis

  • 8.1. Market Dynamics and Trends
    • 8.1.1. Growth Drivers
    • 8.1.2. Restraints
    • 8.1.3. Opportunity
    • 8.1.4. Key Trends
  • 8.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 8.2.1. Key Insights
      • 8.2.1.1. By Component
      • 8.2.1.2. By Embodiment
      • 8.2.1.3. By Technology
      • 8.2.1.4. By Autonomy Level
      • 8.2.1.5. By Application
      • 8.2.1.6. By End User
      • 8.2.1.7. By Country

Chapter 9. Middle East & Africa Market Analysis

  • 9.1. Market Dynamics and Trends
    • 9.1.1. Growth Drivers
    • 9.1.2. Restraints
    • 9.1.3. Opportunity
    • 9.1.4. Key Trends
  • 9.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 9.2.1. Key Insights
      • 9.2.1.1. By Component
      • 9.2.1.2. By Embodiment
      • 9.2.1.3. By Technology
      • 9.2.1.4. By Autonomy Level
      • 9.2.1.5. By Application
      • 9.2.1.6. By End User
      • 9.2.1.7. By Country

Chapter 10. South America Market Analysis

  • 10.1. Market Dynamics and Trends
    • 10.1.1. Growth Drivers
    • 10.1.2. Restraints
    • 10.1.3. Opportunity
    • 10.1.4. Key Trends
  • 10.2. Market Size and Forecast, 2020-2035 (US$ Mn)
    • 10.2.1. Key Insights
      • 10.2.1.1. By Component
      • 10.2.1.2. By Embodiment
      • 10.2.1.3. By Technology
      • 10.2.1.4. By Autonomy Level
      • 10.2.1.5. By Application
      • 10.2.1.6. By End User
      • 10.2.1.7. By Country

Chapter 11. Company Profile (Company Overview, Financial Matrix, Key Product landscape, Key Personnel, Key Competitors, Contact Address, and Business Strategy Outlook)

  • 11.1. Cera
  • 11.2. Cleerly
  • 11.3. CMR Surgical
  • 11.4. Diligent Robotics
  • 11.5. Ekso Bionics
  • 11.6. Intuitive Surgical
  • 11.7. Medtronic
  • 11.8. NDR Medical Technology
  • 11.9. Owkin
  • 11.10. PathAI
  • 11.11. SWORD Health
  • 11.12. Tempus
  • 11.13. Other Prominent Players

Chapter 12. Annexure

  • 12.1. List of Secondary Sources
  • 12.2. Key Country Markets- Macro Economic Outlook/Indicators