휴머노이드 로봇 시장(2027-2037년)

Humanoid robotics has moved decisively out of the demonstration phase. Where the sector was until recently characterised by viral videos and controlled-environment prototypes, it is now defined by volume manufacturing, multi-site customer installations and - for the first time - public listings. Chinese manufacturers dominate both production and consumption, having converted deep domestic supply chains in actuators, sensors, batteries and structural components into a cost position Western competitors have struggled to match. Two Chinese firms account for the majority of global shipments between them, and one has become the first humanoid manufacturer to list on a mainland exchange, giving investors direct exposure to the category and giving the industry its first audited window into unit economics.

The application mix has narrowed usefully. Early ambitions centred on general-purpose capability; commercial reality has concentrated deployment in structured industrial settings - production lines, logistics, warehousing - where tasks are clearly defined and economic value is measurable. Automotive manufacturers have emerged as the pivotal constituency, simultaneously building humanoids, buying them, and converting engine plants to robot production.

Three constraints now govern the trajectory. The first is autonomy: robots remain capable of impressive movement but limited in performing varied tasks reliably over long periods without human intervention. This has shifted competition from locomotion to intelligence, and made embodied AI foundation models - and the physical-world data required to train them - the primary battleground. Partnerships between robot manufacturers and frontier AI developers reflect that repositioning. The second is certification. No standard yet covers bipedal machines in industrial settings, which has pushed a significant cohort of manufacturers toward wheeled platforms certifiable under existing collaborative-robot and mobile-robot regimes. The third is geopolitics: the United States has restricted imports of Chinese humanoids and their components, separating the world's largest producer from one of its largest potential buyers and fragmenting what had been a single global market into distinct regional ones.

The outlook is for continued rapid volume growth alongside steep price deflation, with the balance of value migrating from finished robots toward components and software. Europe has established a credible position built on industrial partnerships and safety-led design rather than scale; Korea and Japan are entering through automotive manufacturing capacity. The decisive question for the coming decade is not whether humanoids will ship in quantity, but whether capability improves fast enough to justify the capital now committed to them.

This report provides a comprehensive analysis of the humanoid robotics industry across technology, applications, markets and competitive landscape, with quantitative forecasts to 2037. It examines a sector that has moved from prototype demonstration into volume manufacturing, assessing where commercial value is being created, which architectures and business models are proving viable, and how trade restriction, certification frameworks and embodied AI development are reshaping the competitive field. Coverage spans the full value chain from actuators, reducers, bearings, sensors and computing platforms through to system integration, deployment economics and end-market adoption. Forecasts are built on reported shipment data and a three-wave adoption model spanning industrial, consumer and medical applications, with a six-year replacement cycle, and are segmented by units, revenue, region, application and component.

Contents include:

• Executive summary - market structure, capital formation and the funding-execution paradox, regional ecosystem dynamics, trade restriction and market segmentation, three-wave adoption framework, strategic implications for leadership
• Introduction - definitions and characteristics, historical evolution, technology readiness by application, commercialisation models and stage of development
• Costs and economics - current and target pricing, cost breakdown by humanoid type, component cost analysis across actuators, structures, power, computing, sensors, end effectors and software, cost per labour hour, ROI timelines, production volume effects, regional cost variation, barriers to cost reduction
• Technology and component analysis - robot design advances, critical components, intelligent control, manufacturing processes, actuators, motors, reducers, screws, bearings, arm effectors and dexterous hands, SoCs, cloud robotics, human-robot interaction, biomimetic design
• Sensors and perception - market forecasts by sensor type, volume and supplier revenue; vision systems, cameras and LiDAR, hybrid approaches, mmWave radar, tactile and force sensors, electronic skin, auditory sensors, IMUs, proximity and range sensing, environmental and biometric sensors, encoders and position sensing, interconnect constraints, sensor fusion
• Artificial intelligence - AI hardware and software, simulation, motion planning and control, foundation models, synthetic data generation, multi-contact planning, end-to-end and multi-modal AI
• Power and energy management - battery technologies, energy harvesting, power distribution, thermal management, wireless charging
• Materials - metals, polymers, composites, elastomers, smart materials, textiles, ceramics, biomaterials, nanomaterials and coatings
• End use markets - healthcare and assistance, education and research, customer service and hospitality, entertainment, manufacturing and industry, automotive, logistics, military and defence, domestic settings
• Market forecasts to 2037 - unified shipment forecast, replacement cycle dynamics, regional distribution, market concentration, revenues, ASP trajectory, battery capacity demand, hardware component markets
• Company profiles - over 100 profiles covering manufacturers, component suppliers and AI developers
• Global regulations and regional market analysis

Companies profiled include 1X Technologies, AeiRobot, Aeolus Robotics, Agibot (Zhiyuan Robot), Agility Robotics, AI² Robotics, AmbiRobotics, Amphenol, Andromeda, Apptronik, Axibo, Baidu, Beyond Imagination, BHRIC (Beijing Humanoid Robot Innovation Center), Boardwalk Robotics, Booster Robotics, Borg Robotics, Boston Dynamics, BridgeDP Robotics, BXI Robotics, BYD, Clone Robotics, Dataa Robotics, Deep Robotics, DeepSeek, Devanthro, Diligent Robotics, Dobot Robotics, Dreame Technology, Electron Robots, Elephant Robotics, Embodied, Enchanted Tools, EngineAI, Engineered Arts, Epoch Robotics, EX Robots, FDROBOT, Figure AI, Formic Technologies, Foundation Future Industries, Fourier Intelligence, Furhat Robotics, Galaxea AI, Galbot, Generation Robots, Generative Bionics, Hanson Robotics, Highlanders, Holiday Robotics, Honda, Humanoid (HMND), IntBot, JAKA Robotics, Kawada Robotics and more.....

Table of Contents

1 EXECUTIVE SUMMARY

• 1.1 2026: the year the humanoid market found its shape
• 1.2 Capital formation: the sector is now financeable at scale
• 1.3 Valuation context and the supply-chain thesis
• 1.4 Three-wave adoption framework
  • 1.4.1 Wave 1 - Industrial Applications (2025-2030)
  • 1.4.2 Wave 2 - Consumer/Developer Applications (2027-2033)
  • 1.4.3 Wave 3 - Medical/Elder Care Applications (2030-2037+)
• 1.5 Commercial Viability
• 1.6 Regional Ecosystem Dynamics
  • 1.6.1 China: Speed, Scale, and State Direction
    • 1.6.1.1 Company Concentration
    • 1.6.1.2 Supply Chain Completeness - The Decisive Advantage
    • 1.6.1.3 Computing Platforms
    • 1.6.1.4 Government Policy
    • 1.6.1.5 Market Scale Advantage
    • 1.6.1.6 Strategic Outlook
    • 1.6.1.7 Computing Platform Competition - Nvidia vs Chinese Alternatives
  • 1.6.2 North America: Vertical Integration and Proprietary Stacks
    • 1.6.2.1 United States
  • 1.6.3 Europe: The Trusted Humanoid Corridor
  • 1.6.4 South Korea
  • 1.6.5 Japan
• 1.7 Trade restriction and market segmentation
• 1.8 Automotive players are leveraging existing expertise to enter the market
• 1.9 Current Applications and Deployment Timeline
• 1.10 Investment Momentum and Market Forecats
  • 1.10.1 Phase 1: Dexterous Hands - The Current Imperative (2025-2027)
  • 1.10.2 Phase 2: Cost Reduction - The Volume Enabler (2026-2030)
  • 1.10.3 Phase 3: Safety & Regulatory - The Medical Gateway (2028-2035)
• 1.11 Market Drivers and Challenges
• 1.12 Strategic Implications for Leadership
• 1.13 Technology Readiness and Future Outlook

2 INTRODUCTION

• 2.1 Humanoid Robots: Definition and Characteristics
• 2.2 Historical Overview and Evolution
• 2.3 Current State of Humanoid Robots in
• 2.4 The Importance of Humanoid Robots
• 2.5 Markets and Applications (TRL)
• 2.6 Three-Wave Framework
  • 2.6.1 Wave 1: Industrial Applications (NOW - 2025-2030)
  • 2.6.2 Wave 2: Consumer/Developer Applications (NEXT - 2027-2033)
  • 2.6.3 Wave 3: Medical/Elder Care Applications (LATER - 2030-2037+)
  • 2.6.4 Strategic Implications for Manufacturers
• 2.7 Models and Stage of Commercial Development
• 2.8 Investments and Funding
  • 2.8.1 The Funding-Execution Paradox
    • 2.8.1.1 Capital Efficiency Analysis
• 2.9 Costs
  • 2.9.1 Current market pricing (2025)
  • 2.9.2 Target pricing (2026-2030)
  • 2.9.3 Cost breakdown by Humanoid Type (Updated 2025)
  • 2.9.4 Component cost analysis
    • 2.9.4.1 Actuators and Motors
    • 2.9.4.2 Structural Components
    • 2.9.4.3 Power Systems
    • 2.9.4.4 Computing and Control Systems
    • 2.9.4.5 Sensors and Perception
    • 2.9.4.6 End Effectors/Hands
    • 2.9.4.7 Software and AI
    • 2.9.4.8 Integration and Assembly
  • 2.9.5 Cost evolution projections to
  • 2.9.6 Cost per labour hour analysis
  • 2.9.7 ROI Timeline Analysis
  • 2.9.8 Production volume impact on costs (2025-2037)
    • 2.9.8.1 Regional cost variations (2025-2037)
  • 2.9.9 Barriers to cost reduction
  • 2.9.10 Cost competitiveness analysis (2025-2037)
• 2.10 Embodied AI models and their effect on sensor requirements
• 2.11 Market Drivers
  • 2.11.1 Advancements in Artificial Intelligence (AI) and Machine Learning (ML)
  • 2.11.2 Labour force shortages
  • 2.11.3 Labour force substitution
  • 2.11.4 Need for Personal Assistance and Companionship
  • 2.11.5 Exploration of Hazardous and Extreme Environments
• 2.12 Challenges
  • 2.12.1 Commercial Challenges
  • 2.12.2 Technical Challenges
• 2.13 Global regulations
• 2.14 Market in Japan
• 2.15 Market in United States
• 2.16 Market in China

3 TECHNOLOGY AND COMPONENT ANALYSIS

• 3.1 Advancements in Humanoid Robot Design
• 3.2 Critical Components
• 3.3 Intelligent Control Systems and Optimization
• 3.4 Advanced Robotics and Automation
• 3.5 Manufacturing
  • 3.5.1 Design and Prototyping
  • 3.5.2 Component Manufacturing
  • 3.5.3 Assembly and Integration
  • 3.5.4 Software Integration and Testing
  • 3.5.5 Quality Assurance and Performance Validation
  • 3.5.6 Challenges
    • 3.5.6.1 Actuators
    • 3.5.6.2 Reducers
    • 3.5.6.3 Thermal management
    • 3.5.6.4 Batteries
    • 3.5.6.5 Cooling
    • 3.5.6.6 Sensors
• 3.6 Brain Computer Interfaces
• 3.7 Robotics and Intelligent Health
  • 3.7.1 Robotic Surgery and Minimally Invasive Procedures
  • 3.7.2 Rehabilitation and Assistive Robotics
  • 3.7.3 Caregiving and Assistive Robots
  • 3.7.4 Intelligent Health Monitoring and Diagnostics
  • 3.7.5 Telemedicine and Remote Health Management
  • 3.7.6 Robotics in Mental Health
• 3.8 Micro-nano Robots
• 3.9 Medical and Rehabilitation Robots
• 3.10 Mechatronics and Robotics
• 3.11 Image Processing, Robotics and Intelligent Vision
  • 3.11.1 Neural Processing Revolution
  • 3.11.2 Spatial Understanding and Navigation
  • 3.11.3 Human-Centered Vision Systems
  • 3.11.4 Learning and Adaptation
• 3.12 Artificial Intelligence and Machine Learning
  • 3.12.1 Overview
  • 3.12.2 AI Hardware and Software
    • 3.12.2.1 Functions
    • 3.12.2.2 Simulation
    • 3.12.2.3 Motion Planning and Control
    • 3.12.2.4 Foundation Models
    • 3.12.2.5 Synthetic Data Generation
    • 3.12.2.6 Multi-contact planning and control
  • 3.12.3 End-to-end AI
  • 3.12.4 Multi-modal AI algorithms
• 3.13 Sensors and Perception Technologies
  • 3.13.1 Humanoid Robots Sensors Market
    • 3.13.1.1 Annual volume of sensors for humanoid robots, 2027–2037
    • 3.13.1.2 Annual supplier revenue by sensor, 2027–2037
    • 3.13.1.3 Annual supplier revenue by sensing category, 2027–2037
    • 3.13.1.4 Cost breakdown by sensor for humanoid robots, 2027 vs
    • 3.13.1.5 Sensor demand by region
    • 3.13.1.6 Sensor demand by platform architecture
  • 3.13.2 Vision Systems
    • 3.13.2.1 Commerical examples
  • 3.13.3 Hybrid LiDAR-camera approaches
  • 3.13.4 Cameras and LiDAR
    • 3.13.4.1 Cameras (RGB, depth, thermal, event-based)
    • 3.13.4.2 Stereo vision and 3D perception
    • 3.13.4.3 Optical character recognition (OCR)
    • 3.13.4.4 Facial recognition and tracking
    • 3.13.4.5 Gesture recognition
    • 3.13.4.6 mmWave Radar
    • 3.13.4.7 How foundation models are changing camera specification
    • 3.13.4.8 Perception sensor requirements under safety certification regimes
  • 3.13.5 Tactile and Force Sensors
    • 3.13.5.1 Value proposition of advanced tactile systems
    • 3.13.5.2 Commercial examples
    • 3.13.5.3 Flexible tactile sensors
    • 3.13.5.4 Tactile sensing for humanoid extremities
    • 3.13.5.5 Tactile sensors (piezoresistive, capacitive, piezoelectric)
    • 3.13.5.6 Force/torque sensors (strain gauges, load cells)
    • 3.13.5.7 Haptic feedback sensors
    • 3.13.5.8 Skin-like sensor arrays
    • 3.13.5.9 Hand degrees of freedom as the leading indicator of tactile demand
    • 3.13.5.10 Tactile sensing as a safety-rated component
    • 3.13.5.11 Tactile data as an input to embodied AI model training
    • 3.13.5.12 Node density, yield and interconnect in electronic skin manufacture
    • 3.13.5.13 Hands and e-skin are different businesses
    • 3.13.5.14 High-payload force-torque sensing: the 100 kg class
    • 3.13.5.15 Mounting position and the effect of wheeled architectures
  • 3.13.6 Auditory Sensors
    • 3.13.6.1 Microphones (array, directional, binaural)
    • 3.13.6.2 Sound Localization and Source Separation
    • 3.13.6.3 Speech Recognition and Synthesis
    • 3.13.6.4 Acoustic Event Detection
  • 3.13.7 Inertial Measurement Units (IMUs)
    • 3.13.7.1 Accelerometers
    • 3.13.7.2 Gyroscopes
    • 3.13.7.3 Magnetometers
    • 3.13.7.4 Attitude and Heading Reference Systems (AHRS)
    • 3.13.7.5 IMU requirements for wheeled versus bipedal architectures
    • 3.13.7.6 IMU grade segmentation and the commoditisation risk
  • 3.13.8 Proximity and Range Sensors
    • 3.13.8.1 Ultrasonic sensors
    • 3.13.8.2 Laser range finders (LiDAR)
    • 3.13.8.3 Radar sensors
    • 3.13.8.4 Time-of-Flight (ToF) sensors
  • 3.13.9 Environmental Sensors
    • 3.13.9.1 Temperature sensors
    • 3.13.9.2 Humidity sensors
    • 3.13.9.3 Gas and chemical sensors
    • 3.13.9.4 Pressure sensors
  • 3.13.10 Biometric Sensors
    • 3.13.10.1 Heart rate sensors
    • 3.13.10.2 Respiration sensors
    • 3.13.10.3 Electromyography (EMG) sensors
    • 3.13.10.4 Electroencephalography (EEG) sensors
  • 3.13.11 Sensor Fusion
    • 3.13.11.1 Kalman Filters
    • 3.13.11.2 Particle Filters
    • 3.13.11.3 Simultaneous Localization and Mapping (SLAM)
    • 3.13.11.4 Object Detection and Recognition
    • 3.13.11.5 Semantic Segmentation
    • 3.13.11.6 Scene Understanding
  • 3.13.12 Encoders and Position Sensors
    • 3.13.12.1 Reduced degree-of-freedom platforms and their effect on encoder demand
    • 3.13.12.2 Encoder qualification and supply security in Chinese programmes
  • 3.13.13 Interconnect, Harnessing and the Physical Limits of Sensor Density
    • 3.13.13.1 Why interconnect constrains sensor count
    • 3.13.13.2 Interconnect suppliers entering humanoid robotics
    • 3.13.13.3 Implications for distributed sensing architectures
• 3.14 Power and Energy Management
  • 3.14.1 Battery Technologies
  • 3.14.2 Challenges
  • 3.14.3 Energy Harvesting and Regenerative Systems
    • 3.14.3.1 Energy Harvesting Techniques
    • 3.14.3.2 Regenerative Braking Systems
    • 3.14.3.3 Hybrid Power Systems
  • 3.14.4 Power Distribution and Transmission
    • 3.14.4.1 Efficient Power Distribution Architectures
    • 3.14.4.2 Advanced Power Electronics and Motor Drive Systems
    • 3.14.4.3 Distributed Power Systems and Intelligent Load Management
  • 3.14.5 Thermal Management
    • 3.14.5.1 Cooling Systems
    • 3.14.5.2 Thermal Modeling and Simulation Techniques
    • 3.14.5.3 Advanced Materials and Coatings
  • 3.14.6 Energy-Efficient Computing and Communication
  • 3.14.7 Cooling architectures
    • 3.14.7.1 Low-Power Computing Architectures
    • 3.14.7.2 Energy-Efficient Communication Protocols and Wireless Technologies
    • 3.14.7.3 Intelligent Power Management Strategies
  • 3.14.8 Wireless Power Transfer and Charging
  • 3.14.9 Energy Optimization and Machine Learning
• 3.15 Actuators
  • 3.15.1 Humanoid robot actuation systems
  • 3.15.2 Actuators in humanoid joint systems
  • 3.15.3 Energy transduction mechanism
• 3.16 Motors
  • 3.16.1 Overview
  • 3.16.2 Frameless motors
  • 3.16.3 Brushed/Brushless Motors
  • 3.16.4 Coreless motors
• 3.17 Reducers
  • 3.17.1 Harmonic reducers
  • 3.17.2 RV (Rotary Vector) reducers
  • 3.17.3 Planetary gear systems
• 3.18 Screws
  • 3.18.1 Screw-based transmission systems
  • 3.18.2 Ball screw assemblies
  • 3.18.3 Planetary Roller Screws
• 3.19 Bearings
  • 3.19.1 Overview
• 3.20 Arm Effectors
  • 3.20.1 Overview
  • 3.20.2 Dexterous hands and tactile sensing
  • 3.20.3 Hot-swappable end effector systems
  • 3.20.4 Challenges
• 3.21 SoCs for Humanoid Robotics
• 3.22 Cloud Robotics and Internet of Robotic Things (IoRT)
• 3.23 Human-Robot Interaction (HRI) and Social Robotics
• 3.24 Biomimetic and Bioinspired Design
• 3.25 Materials for Humanoid Robots
  • 3.25.1 New materials development
  • 3.25.2 Metals
    • 3.25.2.1 Magnesium Alloy
  • 3.25.3 Shape Memory Alloys
  • 3.25.4 Plastics and Polymers
  • 3.25.5 Composites
  • 3.25.6 Elastomers
  • 3.25.7 Smart Materials
  • 3.25.8 Textiles
  • 3.25.9 Ceramics
  • 3.25.10 Biomaterials
  • 3.25.11 Nanomaterials
  • 3.25.12 Coatings
    • 3.25.12.1 Self-healing coatings
    • 3.25.12.2 Conductive coatings
• 3.26 Binding Skin Tissue

4 END USE MARKETS

• 4.1 Market supply chain
• 4.2 Level of commercialization
• 4.3 Healthcare and Assistance
• 4.4 Education and Research
• 4.5 Customer Service and Hospitality
• 4.6 Entertainment and Leisure
• 4.7 Manufacturing and Industry
  • 4.7.1 Overview
    • 4.7.1.1 Assembly and Production
    • 4.7.1.2 Quality Inspection
    • 4.7.1.3 Warehouse Assistance
  • 4.7.2 Automotive
    • 4.7.2.1 Commercial examples
  • 4.7.3 Logistics
    • 4.7.3.1 Warehouse environments
    • 4.7.3.2 Commercial examples
  • 4.7.4 Deployments
    • 4.7.4.1 Deployment Leaders - Automotive
    • 4.7.4.2 Deployment Leaders - Logistics
• 4.8 Military and Defense
• 4.9 Personal Use and Domestic Settings

5 GLOBAL MARKET SIZE (UNITS AND REVENUES) 2024-2037

• 5.1 Market Drivers and Labour Dynamics
• 5.2 Unified Shipments Forecast: Three-Wave Adoption Model
  • 5.2.1 Wave 1: Industrial Applications (2025-2030)
  • 5.2.2 Wave 2: Consumer/Developer Applications (2027-2033)
    • 5.2.2.1 Strategic Importance Beyond Revenue
  • 5.2.3 Wave 3: Medical/Elder Care Applications (2030-2037+)
• 5.3 Replacement Cycle Dynamics
  • 5.3.1 Impact on Market Dynamics
• 5.4 Growth Trajectory Analysis
• 5.5 Regional Distribution Forecast
  • 5.5.1 China's Dominant Position Strengthens Over Time
• 5.6 Market Concentration Evolution
• 5.7 Risk Factors and Sensitivities
• 5.8 Revenues (Total)
  • 5.8.1 Three-Wave Revenue Architecture
    • 5.8.1.1 Wave 1: Industrial Applications (2025-2037)
    • 5.8.1.2 Key Applications and Revenue Drivers
    • 5.8.1.3 ROI Model Supporting Pricing
  • 5.8.2 Wave 2: Consumer and Developer Applications (2026-2037)
    • 5.8.2.1 Breakthrough Products and Price Anchors
    • 5.8.2.2 Market Segments and Revenue Drivers
    • 5.8.2.3 Strategic Value Beyond Direct Revenue
  • 5.8.3 Wave 3: Medical and Elder Care Applications (2031-2037+)
    • 5.8.3.1 Price Point Evolution
    • 5.8.3.2 Sensing and Certification Requirements
    • 5.8.3.3 Deployment Sequencing and Regulatory Pathways
    • 5.8.3.4 Growth Constraints
    • 5.8.3.5 Policy Signals Enabling Future Acceleration
    • 5.8.3.6 ROI Model Supporting Premium Pricing
    • 5.8.3.7 Post-2037 Outlook
  • 5.8.4 Downside Scenarios
• 5.9 Average Selling Price Trajectory and Drivers
  • 5.9.1 ASP Decline by Period
  • 5.9.2 Decomposing ASP Decline Factors
  • 5.9.3 ASP Variance by Wave (2036)
• 5.10 Geographic Revenue Distribution
  • 5.10.1 Revenue by Region of Deployment
    • 5.10.1.1 China
    • 5.10.1.2 North America
    • 5.10.1.3 Europe
    • 5.10.1.4 Japan and South Korea
    • 5.10.1.5 Rest of World
  • 5.10.2 Revenue by Region of Manufacture
  • 5.10.3 Regional Wave Composition
  • 5.10.4 Regional Forecast Risks
• 5.11 Replacement Cycle Revenue Dynamics
  • 5.11.1 Replacement Revenue Emergence
  • 5.11.2 Wave-Specific Replacement Behaviour
  • 5.11.3 Model Retraining as a Replacement Driver
  • 5.11.4 Manufacturer Economics Improvement
• 5.12 Market Structure and Concentration
  • 5.12.1 Concentration Evolution
  • 5.12.2 Public Market Transition
  • 5.12.3 Chinese Manufacturer Revenue Share
  • 5.12.4 Revenue Forecast Confidence Assessment
  • 5.12.5 Sensitivity Analysis
• 5.13 Battery Capacity (GWh) Forecast
  • 5.13.1 Capacity Demand by Industry Segment
  • 5.13.2 Average Capacity per Robot
  • 5.13.3 Capacity Requirements by Application
  • 5.13.4 Forecast Drivers
• 5.14 Hardware Components
  • 5.14.1 Component Cost per Robot
  • 5.14.2 Total Component Market Size
  • 5.14.3 Understanding the Mechanical Dominance of Humanoid BOM
    • 5.14.3.1 Dexterous Hands: The Critical Bottleneck (31% of BOM)
    • 5.14.3.2 Joint Actuators: Mature Technology, High Volume Cost (42% of BOM)
    • 5.14.3.3 Why Semiconductors Are Small Despite Increasing Compute (8% to 5% of BOM)
  • 5.14.4 Interconnect and Harnessing
  • 5.14.5 Strategic Implications for Component Suppliers

6 COMPANY PROFILES 402 (109 company profiles)

7 HUMANOID ROBOTS DEVELOPED BY ACADEMIA

8 RESEARCH METHODOLOGY

9 REFERENCES