The Global Emerging Robotics Market 2027–2037
Emerging robotics comprises the classes of machine that break the founding assumptions of classical industrial robotics — operating outside cages, in unstructured environments, alongside people, or beyond the reach of a reliable communications link — and that consequently depend on perception, learning and onboard decision-making rather than on a pre-programmed path in a controlled cell. Emerging robotics is the fastest-growing segment of the global automation economy and the most widely misunderstood. The market spans seven verticals: warehouse and logistics, defence, humanoids, manufacturing and automation, construction and infrastructure, space, and robotics foundation models. Three structural facts define the decade ahead, and each cuts against the prevailing narrative.
The first is that no general-purpose robot exists. Every commercially deployed system in the world today is task-scripted, teleoperated, or supervised. The most-cited humanoid deployments on earth — at BYD, GXO, Amazon, BMW and Mercedes — amount, in total, to a few hundred machines. The growth is real; general autonomy is not. And the systems operating closest to genuine mission-level autonomy are found not in humanoids but in defence, where GPS-denied and communications-denied conditions have forced the problem to be solved rather than deferred.
The second is that value does not sit where capital is flowing. Actuation — actuators together with dexterous hands, which are themselves actuator assemblies — constitutes the overwhelming majority of a humanoid robot's bill of materials. Semiconductors are a small and shrinking fraction of it, and even the silicon aboard the machine is majority motor-control rather than AI compute. Chinese suppliers hold a decisive structural cost advantage in precisely the mechanical categories that dominate the machine, arising from end-to-end domestic supply chains and near-total control of rare-earth magnets. A Western strategy predicated on owning the intelligence layer is a strategy predicated on owning the smallest part of the robot.
The third is that the binding constraint is physical, not algorithmic. Power, memory and bandwidth — not model quality — determine what a robot can do away from a wall socket and a network connection. A full manipulation stack demands more memory than onboard accelerators can supply, and every watt spent on inference is a watt not spent on motion. The operational compute and memory layer that follows from this is the fastest-growing category in the components market, it has no incumbent, and it appears on no published market map.
The components layer is where the machine's cost actually lives, and it is the least funded part of the industry. That asymmetry is the central investment finding of this report.
The Global Emerging Robotics Market 2027–2037 is a comprehensive analysis of the seven verticals reshaping physical automation, the three-layer stack beneath them, and the capital being deployed against both. Published by Future Markets, Inc., the report combines a full quantitative forecast to 2037 with an unusually direct assessment of what these machines can and cannot presently do. The report introduces the Autonomy Dependency Scale (A0–A4), a classification applied consistently to every company profiled, distinguishing designed capability from observed operating class. It records, for the first time in a market study of this kind, that nothing in commercial deployment operates at A4, that the most autonomous systems on the market are in defence rather than humanoids, and that the gap between vendor claims and field performance is widest precisely where capital is most concentrated.
A detailed bill-of-materials analysis resolves the value-capture dispute that has dominated the sector's commentary. The report demonstrates that actuation accounts for 73% of a humanoid's cost, that semiconductors fall from 8% to 5%, and that cost share, margin and defensibility are three different things — with a 40–60% Chinese cost advantage in the categories that matter most. The forecast covers 2027–2037 across seven verticals, seven component categories, five business models and five regions, in base, conservative and optimistic scenarios, with a full sensitivity analysis identifying the consumer humanoid price threshold as the single largest variable in the market.
Contents:
The first is that no general-purpose robot exists. Every commercially deployed system in the world today is task-scripted, teleoperated, or supervised. The most-cited humanoid deployments on earth — at BYD, GXO, Amazon, BMW and Mercedes — amount, in total, to a few hundred machines. The growth is real; general autonomy is not. And the systems operating closest to genuine mission-level autonomy are found not in humanoids but in defence, where GPS-denied and communications-denied conditions have forced the problem to be solved rather than deferred.
The second is that value does not sit where capital is flowing. Actuation — actuators together with dexterous hands, which are themselves actuator assemblies — constitutes the overwhelming majority of a humanoid robot's bill of materials. Semiconductors are a small and shrinking fraction of it, and even the silicon aboard the machine is majority motor-control rather than AI compute. Chinese suppliers hold a decisive structural cost advantage in precisely the mechanical categories that dominate the machine, arising from end-to-end domestic supply chains and near-total control of rare-earth magnets. A Western strategy predicated on owning the intelligence layer is a strategy predicated on owning the smallest part of the robot.
The third is that the binding constraint is physical, not algorithmic. Power, memory and bandwidth — not model quality — determine what a robot can do away from a wall socket and a network connection. A full manipulation stack demands more memory than onboard accelerators can supply, and every watt spent on inference is a watt not spent on motion. The operational compute and memory layer that follows from this is the fastest-growing category in the components market, it has no incumbent, and it appears on no published market map.
The components layer is where the machine's cost actually lives, and it is the least funded part of the industry. That asymmetry is the central investment finding of this report.
The Global Emerging Robotics Market 2027–2037 is a comprehensive analysis of the seven verticals reshaping physical automation, the three-layer stack beneath them, and the capital being deployed against both. Published by Future Markets, Inc., the report combines a full quantitative forecast to 2037 with an unusually direct assessment of what these machines can and cannot presently do. The report introduces the Autonomy Dependency Scale (A0–A4), a classification applied consistently to every company profiled, distinguishing designed capability from observed operating class. It records, for the first time in a market study of this kind, that nothing in commercial deployment operates at A4, that the most autonomous systems on the market are in defence rather than humanoids, and that the gap between vendor claims and field performance is widest precisely where capital is most concentrated.
A detailed bill-of-materials analysis resolves the value-capture dispute that has dominated the sector's commentary. The report demonstrates that actuation accounts for 73% of a humanoid's cost, that semiconductors fall from 8% to 5%, and that cost share, margin and defensibility are three different things — with a 40–60% Chinese cost advantage in the categories that matter most. The forecast covers 2027–2037 across seven verticals, seven component categories, five business models and five regions, in base, conservative and optimistic scenarios, with a full sensitivity analysis identifying the consumer humanoid price threshold as the single largest variable in the market.
Contents:
- Executive summary — key findings, the three structural claims, and what changed since 2026
- Introduction and taxonomy — defining emerging robotics; the market map; the three-layer stack (components > platforms > intelligence)
- The autonomy gap — the Autonomy Dependency Scale A0–A4; designed versus observed autonomy by vertical; the data scarcity problem; why defence leads
- Value capture and the bill of materials — full BOM decomposition; the actuation share; the semiconductor share; cost share versus margin versus defensibility; the Chinese cost advantage quantified
- Supply chain and components — actuators and transmissions; dexterous hands (deep dive: $629M > $19.9bn, 41.3% CAGR); sensors; power systems; edge silicon; rare-earth and battery concentration; operational compute and memory
- Robotics foundation models — the merchant model market; the vertical-integration squeeze; the data problem; why the layer's share of value declines
- Humanoids — the three-wave adoption model; shipments versus ASP; the deployment reality check; concentration and the relocation of the market to China
- Warehouse and logistics — sub-segments; the pick-rate frontier; cost per pick; the RaaS transition; why the humanoid loses here
- Manufacturing and automation — the cell cost stack; batch-size economics; the skilled-trade shortage; the programming-cost curve
- Defence — attritable mass; designed versus observed autonomy in Ukraine; the power budget; counter-UAS economics; autonomy licensing
- Space — light-time delay and the collapse of teleoperation; the radiation-hardened compute gap; on-orbit servicing, ISAM and debris removal
- Construction and infrastructure — the fifty-year productivity divergence; the automation frontier; why solar is the wedge
- Market forecasts 2027–2037 — by vertical, component, business model, region and units; three scenarios; sensitivity analysis; the double-counting convention
- Investment and competitive landscape — VC trajectory; capital allocation versus revenue opportunity; the starved components layer; exit environment
- Company profiles — 168 companies with autonomy classification. Companies Profiled include 1X Technologies, ABB, Agibot, Agile Robots, Agility Robotics, AheadForm, AIRSKIN, AI? Robotics (AI2), AmbiRobotics, Anduril Industries, ANYbotics, Apptronik, ARX Robotics, Aubo Robotics, Augmentus, Baidu, BHRIC (Beijing Humanoid Robot Innovation Center), Boardwalk Robotics, Boost Robotics, Booster Robotics, Boston Dynamics, BridgeDP Robotics, Bright Machines, BRINC, Built Robotics, BXI Robotics, Charge Robotics, ClearPath Robotics, ClearSpace, Clone Robotics, Cognibotics, Contoro Robotics, Cosmic Robotics, Covariant, Daimon Robotics, Dataa Robotics, Deep Robotics, DeepCloud AI, Dexory, Dexterity, Diligent Robotics, Dobot Robotics, Doosan Robotics, dRobotics, Dusty Robotics, Dyna Robotics, Electron Robots, Elephant Robotics, EngineAI, Epoch Robotics, Eureka Robotics, EX Robots, Exotec, Fanuc, FBR (Hadrian X), FDROBOT, FESTO, Field AI, Figure AI, Fluid Wire Robotics, Formant, Forterra, ForwardX, Foundation, Fourier Intelligence, Franka Emika, Galaxea AI, Galbot, Gecko Robotics, Ghost Robotics, GITAI, GrayMatter Robotics, Hadrian, HavocAI, HEBI Robotics, Honda, Humanoid, Hypercraft, Icarus Robotics, Inivation, IntBot, intuiCell, Jacobi Robotics and more....
1 EXECUTIVE SUMMARY
1.1 The Market in Summary
1.2 Principal Findings
1.2.1 No General-Purpose Robot Exists, and None Is Close
1.2.2 The Most Autonomous Systems on the Market Map Are in Defence, Not in Humanoids
1.2.3 Is Value Concentrated in Actuation, and is the Concentration Structural
1.2.4 The Binding Constraint Is Physical, Not Algorithmic
1.3 Market Forecast Summary
1.4 Implications for Positioning
2 INTRODUCTION: MARKET DEFINITION, TAXONOMY AND THE EMERGING ROBOTICS STACK
2.1 Defining Emerging Robotics
2.2 The Commercial Consequence of the Definition
2.2.1 The Five Competing Business Models
2.3 The Seven-Vertical Taxonomy
2.3.1 Foundation Models as a Layer Rather Than a Vertical
2.3.2 Humanoids as a Form Factor Rather Than a Market
2.3.3 The Growing Primacy of Defence
2.4 The Emerging Robotics Stack
2.4.1 The Operational Compute and Memory Layer
2.4.2 The Physical Components Layer
2.5 Scope Exclusions
2.6 Methodology and Basis of Estimates
2.6.1 The Treatment of Pilots
2.6.2 The Treatment of Teleoperated Systems
2.6.3 The Treatment of Replacement Demand
3 THE AUTONOMY GAP: WHY NO GENERAL-PURPOSE ROBOT EXISTS
3.1 The Present State of General-Purpose Capability
3.2 The Four Senses of "Autonomous"
3.2.1 The Consequence of Definitional Slippage
3.3 Teleoperation as Data Supply
3.3.1 Teleoperation Within the Cost of Goods Sold
3.3.2 The Economics of the Pilot
3.3.3 The Step Function
3.4 The Autonomy Dependency Classification
3.4.1 Application Across the Market Map
3.4.2 The Primacy of Defence in Demonstrated Autonomy
3.4.3 The Constraint Is Not Model Quality
3.5 The Infrastructure Dependency Problem
3.5.1 Availability
3.5.2 The Onboard Memory Wall
3.5.3 Accountability and Operational Memory
3.6 The Counter-Argument
3.6.1 Improving Connectivity
3.6.2 The Trajectory of Onboard Silicon
3.6.3 An Institutional Answer to Accountability
3.6.4 Assessment
3.7 Consequences for the Forecast
3.7.1 Humanoids
3.7.2 Defence
3.7.3 Foundation Models
4 VALUE CAPTURE: BILL-OF-MATERIALS ECONOMICS
4.1 The Dispute
4.2 What the Bill of Materials Actually Shows
4.2.1 The Mechanical Dominance of Cost
4.2.2 The Concentration Is Structural, Not Transitional
4.2.3 Even the Silicon Is Mostly Actuation
4.3 The Error Common to Both Camps
4.3.1 Cost Share Is Not Margin Capture
4.3.2 Margin Capture Is Not Defensibility
4.4 Where the Defensible Positions Actually Lie
4.4.1 High-Performance Actuation, Not Actuation
4.4.2 Operational Compute and Memory
4.4.3 Integration, Which Nobody Is Arguing For
5 THE SUPPLY CHAIN AND COMPONENTS LAYER
5.1 The Layer the Market Map Omits
5.2 Actuators, Harmonic Drives and Transmissions
5.3 End Effectors and Dexterous Hands
5.3.1 The Dexterous Hand Market
5.3.2 Why Hands Cost What They Cost
5.3.3 Demand by Industry Application
5.3.4 The Dexterity-Price Frontier
5.3.5 Competitive Implications
5.4 Sensors and Perception
5.5 Power Systems and Operational Energy
5.5.1 Power as Product
5.6 Semiconductors and Edge Compute
5.6.1 The Memory Wall
5.6.2 The Power Budget Trap
5.7 Operational Memory and Verifiable Autonomy
5.7.1 The Requirement
5.7.2 The Market Position
5.7.3 The Bear Case
5.8 Geographic Concentration and Chokepoints
6 FOUNDATION MODELS AND ROBOT LEARNING
6.1 The Layer and Its Ambition
6.2 The Data Problem
6.2.1 The Consequence for the Business Model
6.2.2 Simulation as Partial Escape
6.3 The Vertical Integration Squeeze
6.4 The Inference Constraint
6.5 Competitive Landscape
7 HUMANOIDS
7.1 Market Overview
7.2 The Deployment Reality
7.3 The Three-Wave Structure
7.3.1 Wave 1: Industrial
7.3.2 Wave 2: Consumer and Developer
7.3.3 Wave 3: Medical and Assistive
7.4 The Capability Gap
7.4.1 The Manipulation Bottleneck
7.4.2 The Step Function
7.5 Competitive Structure
7.5.1 Why China Wins on the Current Cost Structure
8 WAREHOUSE AND LOGISTICS
8.1 Market Overview
8.2 The Task-Scripted Ceiling, and Why It Does Not Matter Here
8.3 The Economics: Cost Per Pick
8.4 What the Buyer Is Actually Buying
8.5 Business Model: The RaaS Transition
8.6 Competitive Landscape
9 MANUFACTURING AND AUTOMATION
9.1 Market Overview
9.2 The Real Cost of a Robot Is Not the Robot
9.3 The Batch-Size Window
9.4 The Labour Constraint Is Specific, Not General
9.5 Competitive Landscape
10 SPACE ROBOTICS
10.1 Market Overview
10.2 Why Space Cannot Cheat
10.3 The Radiation-Hardened Compute Gap
10.4 The Autonomy Reality
10.5 Competitive Landscape
11 CONSTRUCTION AND INFRASTRUCTURE
11.1 Market Overview
11.2 Why Construction Resisted
11.2.1 The Site Is the Anti-Warehouse
11.2.2 The Buyer Cannot Fund It
11.2.3 The Labour Question Is Political
11.3 The Automation Frontier
11.4 Competitive Landscape
12 DEFENCE AND SECURITY
12.1 Market Overview
12.2 The Procurement Inversion
12.3 The Gap Between Designed and Observed Autonomy
12.4 The Domains
12.4.1 Ground
12.4.2 Air
12.4.3 Maritime
12.4.4 Counter-UAS
12.5 Company Landscape
13 COMPANY PROFILES (168 COMPANY PROFILES)
14 REFERENCES
1.1 The Market in Summary
1.2 Principal Findings
1.2.1 No General-Purpose Robot Exists, and None Is Close
1.2.2 The Most Autonomous Systems on the Market Map Are in Defence, Not in Humanoids
1.2.3 Is Value Concentrated in Actuation, and is the Concentration Structural
1.2.4 The Binding Constraint Is Physical, Not Algorithmic
1.3 Market Forecast Summary
1.4 Implications for Positioning
2 INTRODUCTION: MARKET DEFINITION, TAXONOMY AND THE EMERGING ROBOTICS STACK
2.1 Defining Emerging Robotics
2.2 The Commercial Consequence of the Definition
2.2.1 The Five Competing Business Models
2.3 The Seven-Vertical Taxonomy
2.3.1 Foundation Models as a Layer Rather Than a Vertical
2.3.2 Humanoids as a Form Factor Rather Than a Market
2.3.3 The Growing Primacy of Defence
2.4 The Emerging Robotics Stack
2.4.1 The Operational Compute and Memory Layer
2.4.2 The Physical Components Layer
2.5 Scope Exclusions
2.6 Methodology and Basis of Estimates
2.6.1 The Treatment of Pilots
2.6.2 The Treatment of Teleoperated Systems
2.6.3 The Treatment of Replacement Demand
3 THE AUTONOMY GAP: WHY NO GENERAL-PURPOSE ROBOT EXISTS
3.1 The Present State of General-Purpose Capability
3.2 The Four Senses of "Autonomous"
3.2.1 The Consequence of Definitional Slippage
3.3 Teleoperation as Data Supply
3.3.1 Teleoperation Within the Cost of Goods Sold
3.3.2 The Economics of the Pilot
3.3.3 The Step Function
3.4 The Autonomy Dependency Classification
3.4.1 Application Across the Market Map
3.4.2 The Primacy of Defence in Demonstrated Autonomy
3.4.3 The Constraint Is Not Model Quality
3.5 The Infrastructure Dependency Problem
3.5.1 Availability
3.5.2 The Onboard Memory Wall
3.5.3 Accountability and Operational Memory
3.6 The Counter-Argument
3.6.1 Improving Connectivity
3.6.2 The Trajectory of Onboard Silicon
3.6.3 An Institutional Answer to Accountability
3.6.4 Assessment
3.7 Consequences for the Forecast
3.7.1 Humanoids
3.7.2 Defence
3.7.3 Foundation Models
4 VALUE CAPTURE: BILL-OF-MATERIALS ECONOMICS
4.1 The Dispute
4.2 What the Bill of Materials Actually Shows
4.2.1 The Mechanical Dominance of Cost
4.2.2 The Concentration Is Structural, Not Transitional
4.2.3 Even the Silicon Is Mostly Actuation
4.3 The Error Common to Both Camps
4.3.1 Cost Share Is Not Margin Capture
4.3.2 Margin Capture Is Not Defensibility
4.4 Where the Defensible Positions Actually Lie
4.4.1 High-Performance Actuation, Not Actuation
4.4.2 Operational Compute and Memory
4.4.3 Integration, Which Nobody Is Arguing For
5 THE SUPPLY CHAIN AND COMPONENTS LAYER
5.1 The Layer the Market Map Omits
5.2 Actuators, Harmonic Drives and Transmissions
5.3 End Effectors and Dexterous Hands
5.3.1 The Dexterous Hand Market
5.3.2 Why Hands Cost What They Cost
5.3.3 Demand by Industry Application
5.3.4 The Dexterity-Price Frontier
5.3.5 Competitive Implications
5.4 Sensors and Perception
5.5 Power Systems and Operational Energy
5.5.1 Power as Product
5.6 Semiconductors and Edge Compute
5.6.1 The Memory Wall
5.6.2 The Power Budget Trap
5.7 Operational Memory and Verifiable Autonomy
5.7.1 The Requirement
5.7.2 The Market Position
5.7.3 The Bear Case
5.8 Geographic Concentration and Chokepoints
6 FOUNDATION MODELS AND ROBOT LEARNING
6.1 The Layer and Its Ambition
6.2 The Data Problem
6.2.1 The Consequence for the Business Model
6.2.2 Simulation as Partial Escape
6.3 The Vertical Integration Squeeze
6.4 The Inference Constraint
6.5 Competitive Landscape
7 HUMANOIDS
7.1 Market Overview
7.2 The Deployment Reality
7.3 The Three-Wave Structure
7.3.1 Wave 1: Industrial
7.3.2 Wave 2: Consumer and Developer
7.3.3 Wave 3: Medical and Assistive
7.4 The Capability Gap
7.4.1 The Manipulation Bottleneck
7.4.2 The Step Function
7.5 Competitive Structure
7.5.1 Why China Wins on the Current Cost Structure
8 WAREHOUSE AND LOGISTICS
8.1 Market Overview
8.2 The Task-Scripted Ceiling, and Why It Does Not Matter Here
8.3 The Economics: Cost Per Pick
8.4 What the Buyer Is Actually Buying
8.5 Business Model: The RaaS Transition
8.6 Competitive Landscape
9 MANUFACTURING AND AUTOMATION
9.1 Market Overview
9.2 The Real Cost of a Robot Is Not the Robot
9.3 The Batch-Size Window
9.4 The Labour Constraint Is Specific, Not General
9.5 Competitive Landscape
10 SPACE ROBOTICS
10.1 Market Overview
10.2 Why Space Cannot Cheat
10.3 The Radiation-Hardened Compute Gap
10.4 The Autonomy Reality
10.5 Competitive Landscape
11 CONSTRUCTION AND INFRASTRUCTURE
11.1 Market Overview
11.2 Why Construction Resisted
11.2.1 The Site Is the Anti-Warehouse
11.2.2 The Buyer Cannot Fund It
11.2.3 The Labour Question Is Political
11.3 The Automation Frontier
11.4 Competitive Landscape
12 DEFENCE AND SECURITY
12.1 Market Overview
12.2 The Procurement Inversion
12.3 The Gap Between Designed and Observed Autonomy
12.4 The Domains
12.4.1 Ground
12.4.2 Air
12.4.3 Maritime
12.4.4 Counter-UAS
12.5 Company Landscape
13 COMPANY PROFILES (168 COMPANY PROFILES)
14 REFERENCES
LIST OF TABLES
Table 1. Global emerging robotics market by vertical, 2027–2037 (US$ billion).
Table 2. Classical versus emerging robotics: the four broken assumptions.
Table 3. The seven verticals of emerging robotics.
Table 4. The emerging robotics stack
Table 5. Four distinct claims advanced under a single word.
Table 6. The three questions, and the answers the bill of materials supplies.
Table 7. Global dexterous hand market forecast, 2027–2037.
Table 8. Dexterous hand requirements by industry application.
Table 9. Selected foundation-model and robot-learning companies.
Table 10. Leading humanoid manufacturers, 2027.
Table 11. Warehouse and logistics robotics: selected companies.
Table 12. Manufacturing and automation robotics: selected companies.
Table 13. Space robotics: selected companies.
Table 14. Construction and infrastructure robotics: selected companies.
Table 15. Defence and security robotics: selected companies.
Table 1. Global emerging robotics market by vertical, 2027–2037 (US$ billion).
Table 2. Classical versus emerging robotics: the four broken assumptions.
Table 3. The seven verticals of emerging robotics.
Table 4. The emerging robotics stack
Table 5. Four distinct claims advanced under a single word.
Table 6. The three questions, and the answers the bill of materials supplies.
Table 7. Global dexterous hand market forecast, 2027–2037.
Table 8. Dexterous hand requirements by industry application.
Table 9. Selected foundation-model and robot-learning companies.
Table 10. Leading humanoid manufacturers, 2027.
Table 11. Warehouse and logistics robotics: selected companies.
Table 12. Manufacturing and automation robotics: selected companies.
Table 13. Space robotics: selected companies.
Table 14. Construction and infrastructure robotics: selected companies.
Table 15. Defence and security robotics: selected companies.
LIST OF FIGURES
Figure 1. The Emerging Robotics Market Map.
Figure 2. The global emerging robotics market by vertical, 2027–2037 (US$ billion).
Figure 3. Vertical positioning: 2027 market size against 2027–2037 revenue CAGR, with bubble area proportional to 2037 revenue.
Figure 4. Humanoid bill-of-materials composition, 2027 and 2037, by share of total.
Figure 5. The Autonomy Dependency classification (A0 to A4)
Figure 6. Autonomy class attained by vertical, 2027: prevailing class of deployed systems versus best-in-class demonstrated.
Figure 7. The onboard memory wall: robotic workload memory demand against the capacity of onboard accelerators.
Figure 8. Humanoid bill-of-materials composition, 2027 and 2037, by share of total.
Figure 9. Component cost per robot, indexed to 2027, showing differential rates of decline.
Figure 10. Semiconductor content per humanoid robot: what the silicon in a robot actually is.
Figure 11. Cost share against estimated gross margin, by component category, 2027.
Figure 12. The components layer: total addressable market by category, 2027–2037 (US$ billion).
Figure 13. Global dexterous hand market: unit shipments and revenue, 2027–2037.
Figure 14. Dexterous hand demand by industry application, 2027 and 2037.
Figure 15. The dexterity-price frontier: degrees of freedom required against price ceiling, by application, with bubble area proportional to 2037 demand share.
Figure 16. The power budget trap: operational runtime against onboard compute power draw, 2027 and 2037 battery packs.
Figure 17. Estimated Chinese share of global supply, by component category.
Figure 18. Training data availability by modality: the robot manipulation data deficit.
Figure 19. Foundation-model layer revenue by business model, 2027–2037.
Figure 20. Flagship commercial humanoid deployments: units in the field.
Figure 21. The humanoid market by adoption wave, 2027–2037 (US$ billion).
Figure 22. Humanoid unit shipments against average selling price, 2027–2037.
Figure 23. Humanoid market concentration and Chinese share of unit volume, 2025–2037.
Figure 24. Warehouse and logistics robotics by sub-segment, 2027–2037 (US$ billion).
Figure 25. The pick-rate frontier: sustained pick rate against fully-loaded cost per pick.
Figure 26. Warehouse robotics revenue by business model, 2027–2037.
Figure 27. Manufacturing and automation robotics by sub-segment, 2027–2037 (US$ billion).
Figure 28. The fully-installed cost stack of one robotic work cell, 2027 and 2037.
Figure 29. Cost per part against batch size: the addressable window for flexible robotic automation.
Figure 30. Space robotics by segment, 2027–2037 (US$ billion).
Figure 31. Round-trip command latency by destination, and the collapse of teleoperation.
Figure 32. Onboard AI compute: commercial edge silicon, radiation-hardened space-qualified silicon, and the requirement for autonomous rendezvous and in-space assembly.
Figure 33. Labour productivity, manufacturing against construction, indexed to 1970.
Figure 34. The construction automation frontier: task repeatability against site-to-site variability.
Figure 35. Construction and infrastructure robotics by segment, 2027–2037 (US$ billion).
Figure 36. Defence robotics market by domain, 2027–2037 (US$ billion).
Figure 37. Estimated unit cost by platform, against the attritability threshold.
Figure 38. Designed autonomy class against the class observed in operational use.
Figure 39. NEO.
Figure 40. RAISE-A1.
Figure 41. Agibot product line-up.
Figure 42. Digit humanoid robot.
Figure 43. ANYbotics robot.
Figure 44. Apptronick Apollo.
Figure 45. Aubo Robotics - i series.
Figure 46. Alex.
Figure 47. BR002.
Figure 48. Atlas.
Figure 49. XR-4.
Figure 50. Deep Robotics all weather robot.
Figure 51. Mercury X1.
Figure 52. Prototype Ex-Robots humanoid robots.
Figure 53. Figure.ai humanoid robot.
Figure 54. Figure 02 humanoid robot.
Figure 55. GR-1.
Figure 56. Honda ASIMO.
Figure 57. HMND 01 Alpha.
Figure 58. IntuiCell quadruped robot.
Figure 59. Kaleido.
Figure 60. Forerunner.
Figure 61. Keyper.
Figure 62. KUKA - LBR iiwa series.
Figure 63. Kuafu.
Figure 64. CL-1.
Figure 65. MagicHand S01
Figure 66. Monumental construction robot.
Figure 67. Neura Robotics - Cognitive Cobots.
Figure 68. Omron - TM5-700 and TM5X-700.
Figure 69. Tora-One.
Figure 70. HUBO2.
Figure 71. XBot-L.
Figure 72. Sanctuary AI Phoenix.
Figure 73. Astribot S1.
Figure 74. Staubli - TX2touch series.
Figure 75. Tesla Optimus Gen 2.
Figure 76. Toyota T-HR3
Figure 77. UBTECH Walker.
Figure 78. G1 foldable robot.
Figure 79. Unitree H1.
Figure 80. WANDA.
Figure 81. CyberOne.
Figure 82. PX5.
Figure 1. The Emerging Robotics Market Map.
Figure 2. The global emerging robotics market by vertical, 2027–2037 (US$ billion).
Figure 3. Vertical positioning: 2027 market size against 2027–2037 revenue CAGR, with bubble area proportional to 2037 revenue.
Figure 4. Humanoid bill-of-materials composition, 2027 and 2037, by share of total.
Figure 5. The Autonomy Dependency classification (A0 to A4)
Figure 6. Autonomy class attained by vertical, 2027: prevailing class of deployed systems versus best-in-class demonstrated.
Figure 7. The onboard memory wall: robotic workload memory demand against the capacity of onboard accelerators.
Figure 8. Humanoid bill-of-materials composition, 2027 and 2037, by share of total.
Figure 9. Component cost per robot, indexed to 2027, showing differential rates of decline.
Figure 10. Semiconductor content per humanoid robot: what the silicon in a robot actually is.
Figure 11. Cost share against estimated gross margin, by component category, 2027.
Figure 12. The components layer: total addressable market by category, 2027–2037 (US$ billion).
Figure 13. Global dexterous hand market: unit shipments and revenue, 2027–2037.
Figure 14. Dexterous hand demand by industry application, 2027 and 2037.
Figure 15. The dexterity-price frontier: degrees of freedom required against price ceiling, by application, with bubble area proportional to 2037 demand share.
Figure 16. The power budget trap: operational runtime against onboard compute power draw, 2027 and 2037 battery packs.
Figure 17. Estimated Chinese share of global supply, by component category.
Figure 18. Training data availability by modality: the robot manipulation data deficit.
Figure 19. Foundation-model layer revenue by business model, 2027–2037.
Figure 20. Flagship commercial humanoid deployments: units in the field.
Figure 21. The humanoid market by adoption wave, 2027–2037 (US$ billion).
Figure 22. Humanoid unit shipments against average selling price, 2027–2037.
Figure 23. Humanoid market concentration and Chinese share of unit volume, 2025–2037.
Figure 24. Warehouse and logistics robotics by sub-segment, 2027–2037 (US$ billion).
Figure 25. The pick-rate frontier: sustained pick rate against fully-loaded cost per pick.
Figure 26. Warehouse robotics revenue by business model, 2027–2037.
Figure 27. Manufacturing and automation robotics by sub-segment, 2027–2037 (US$ billion).
Figure 28. The fully-installed cost stack of one robotic work cell, 2027 and 2037.
Figure 29. Cost per part against batch size: the addressable window for flexible robotic automation.
Figure 30. Space robotics by segment, 2027–2037 (US$ billion).
Figure 31. Round-trip command latency by destination, and the collapse of teleoperation.
Figure 32. Onboard AI compute: commercial edge silicon, radiation-hardened space-qualified silicon, and the requirement for autonomous rendezvous and in-space assembly.
Figure 33. Labour productivity, manufacturing against construction, indexed to 1970.
Figure 34. The construction automation frontier: task repeatability against site-to-site variability.
Figure 35. Construction and infrastructure robotics by segment, 2027–2037 (US$ billion).
Figure 36. Defence robotics market by domain, 2027–2037 (US$ billion).
Figure 37. Estimated unit cost by platform, against the attritability threshold.
Figure 38. Designed autonomy class against the class observed in operational use.
Figure 39. NEO.
Figure 40. RAISE-A1.
Figure 41. Agibot product line-up.
Figure 42. Digit humanoid robot.
Figure 43. ANYbotics robot.
Figure 44. Apptronick Apollo.
Figure 45. Aubo Robotics - i series.
Figure 46. Alex.
Figure 47. BR002.
Figure 48. Atlas.
Figure 49. XR-4.
Figure 50. Deep Robotics all weather robot.
Figure 51. Mercury X1.
Figure 52. Prototype Ex-Robots humanoid robots.
Figure 53. Figure.ai humanoid robot.
Figure 54. Figure 02 humanoid robot.
Figure 55. GR-1.
Figure 56. Honda ASIMO.
Figure 57. HMND 01 Alpha.
Figure 58. IntuiCell quadruped robot.
Figure 59. Kaleido.
Figure 60. Forerunner.
Figure 61. Keyper.
Figure 62. KUKA - LBR iiwa series.
Figure 63. Kuafu.
Figure 64. CL-1.
Figure 65. MagicHand S01
Figure 66. Monumental construction robot.
Figure 67. Neura Robotics - Cognitive Cobots.
Figure 68. Omron - TM5-700 and TM5X-700.
Figure 69. Tora-One.
Figure 70. HUBO2.
Figure 71. XBot-L.
Figure 72. Sanctuary AI Phoenix.
Figure 73. Astribot S1.
Figure 74. Staubli - TX2touch series.
Figure 75. Tesla Optimus Gen 2.
Figure 76. Toyota T-HR3
Figure 77. UBTECH Walker.
Figure 78. G1 foldable robot.
Figure 79. Unitree H1.
Figure 80. WANDA.
Figure 81. CyberOne.
Figure 82. PX5.
