ロボット基盤モデル市場シェア分析、業界動向と統計、成長予測　2026-2031年

ロボット基盤モデル（RFM）市場は2025年に9,746万米ドル、2026年に1億4,401万米ドル規模となり、2026年から2031年に年平均成長率（CAGR）40.48%で成長し、2031年には7億8,786万米ドルに達するとMordor Intelligenceでは予測しています。同市場は現在、研究室レベルでの開発段階から、倉庫、工場、医療施設などでの商用利用へと移行しつつあります。製造・物流業界における慢性的な人手不足を背景に、最小限のプログラミングで多様なタスクを処理できるシステムへの需要が高まっています。企業は単一のロボット設計に依存するのではなく、モデルプラットフォーム、データ収集、シミュレーション、導入ツールへの投資を強化しています。オープンモデルの普及により、小規模なインテグレーターも技術を利用しやすくなっていますが、一方で、モデルのアーキテクチャ単独での差別化は困難になりつつあります。ロボット基盤モデル（RFM）市場は依然として、特にロボットが人と近接して作業する環境において、実環境での学習データ、安全性検証、法的責任の規定といった課題に直面しています。

Mordor Intelligence（モードーインテリジェンス）「ロボット基盤モデル市場シェア分析、業界動向と統計、成長予測　2026-2031年 – Robotics Foundation Models – Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2026 – 2031)」はロボット基盤モデルの世界市場を調査し、主要セグメント別に分析・予測を行っています。

調査対象セグメント

• モデルアーキテクチャ
  • 視覚・言語・行動（VLA）モデル
  • 身体性推論モデル
  • 世界モデル
  • その他のモデルアーキテクチャ（行動ポリシーモデル、クロスエンボディメント制御モデル、ツール連携モデル）
• 導入形態
  • クラウド型
  • オンプレミス型
• 用途
  • 倉庫でのピッキングおよび仕分け
  • 産業用組立作業
  • マテリアルハンドリングおよび梱包
  • 移動型点検およびナビゲーション
  • 家庭内サービス業務
  • その他の用途（商業サービスにおける対人対応、医療・介護支援、農業・現場作業、防衛・警備業務、危険環境下での作業、研究・教育開発）
• エンドユーザーの業種別
  • 製造業者
  • 物流・倉庫事業者
  • システムインテグレーター
  • 医療・介護事業者
  • 防衛・警備機関
  • その他の業種
• 地域
  • 北米
    • 米国
    • カナダ
    • メキシコ
  • 南米
    • ブラジル
    • アルゼンチン
    • チリ
    • その他の南米
  • 欧州
    • ドイツ
    • 英国
    • フランス
    • イタリア
    • ロシア
    • その他の欧州
  • アジア太平洋地域
    • 中国
    • 日本
    • 韓国
    • インド
    • 東南アジア
    • その他のアジア太平洋地域
  • 中東
    • トルコ
    • イスラエル
    • GCC諸国
    • その他の中東
  • アフリカ
    • 南アフリカ
    • エジプト
    • その他のアフリカ

レポートの主なポイント

• モデルアーキテクチャ別では、2025年の市場において「Vision-Language-Action（視覚・言語・行動）」モデルが収益の54.67%を占めました。一方、「Embodied Reasoning（身体性推論）」モデルは、2031年まで年平均成長率（CAGR）46.53%で拡大すると予測されています。
• 導入形態別では、クラウドベースの導入が2025年の収益の57.26%を占め、2031年までCAGR 43.61%で拡大すると予測されています。
• 用途別では、倉庫でのピッキングおよび仕分け作業が2025年の収益の23.16%を占めました。一方、家庭内サービス業務への適用は、2031年までCAGR 46.32%で拡大すると予測されています。エンドユーザー別に見ると、2025年時点では製造業者が収益シェアの35.21%を占めており、医療機関については2031年まで年平均成長率（CAGR）47.27%で拡大すると予測されています。
• 地域別では、2025年のロボット基盤モデル（ロボティクス・ファウンデーション・モデル）市場において北米が収益シェアの45.74%を占め、アジア太平洋地域は2031年までCAGR 46.84%で拡大すると予測されています。

モデルアーキテクチャ別：VLAモデルが主導し、推論アーキテクチャが能力を拡張

2025年、ロボット基盤モデル（ロボティクス・ファウンデーション・モデル）市場において、VLA（Vision-Language-Action：視覚・言語・行動）モデルが54.67%のシェアを占めました。この優位性は、画像、言語によるタスク記述、およびロボットの状態データを単一のバックボーンで処理できる能力に裏打ちされています。この設計により、認識、計画、行動といった個別のコンポーネントを複雑に連携させることなく、指示に従った動作が可能になります。NVIDIAは「GR00T N1.7」を、汎用的なヒューマノイド・ロボットのスキルに対応する、商用利用可能なオープンなVLAモデルとして発表し、Unitree RoboticsやAgile Robotsといったトレーニング・パートナーを明らかにしました[4]。産業現場における既存の導入基盤は、反復的なハンドリングや組み立て作業が行われる環境において、VLAモデルが商用展開を進めるための明確な道筋となっています。

ワールドモデルの収益規模は比較的小さいものの、計画やテストのための物理環境を表現できるという点で依然として重要です。NVIDIA-Medtechは、同社の「Cosmos-H-Surgical-Simulator」が9種類の外科手術プラットフォームにおけるロボットの運動学データからリアルな手術映像を生成し、実機導入前の検証を支援できると述べています。エンボディド・推論（Embodied Reasoning）モデルは、2031年まで年平均成長率（CAGR）46.53%で拡大すると予測されており、これはアーキテクチャ・セグメントの中で最も高い成長率です。Physical Intelligence社は、同社の「π0.7」モデルが、タスク特化型の学習データを使用せずに、コーヒーの準備、洗濯物の折り畳み、箱の組み立てといったタスクにおいて、専用システムと同等の性能を発揮したと報告しています。行動ポリシー、クロス・エンボディメント制御、およびツール・オーケストレーション・モデルは、より限定的なニーズに対応するものです。開発フレームワークの進化により、手頃な価格での導入や適応性の高い制御ポリシーを求める小規模なインテグレーターにとっても、これらのモデルへのアクセスが容易になっています。

エンドユーザー別：製造業が導入規模を牽引、医療・ヘルスケア分野は急速に拡大

2025年時点において、製造業は収益の35.21%を占め、最大のエンドユーザー層であり続けました。自動車メーカー、電子機器組立業者、食品加工業者などは、製品の多様化や生産サイクルの短期化に伴い、固定的なプログラミングでは効率が低下するため、柔軟に対応可能な自動化技術を必要としています。基盤モデル（ファウンデーションモデル）は、単なる人件費の削減にとどまらず、稼働条件の変化に対してより高い柔軟性を提供します。ヒューマノイドや汎用システムの商用導入が進んでいることは、これらの技術が製造や物流の現場において、試験的な運用段階を超えつつあることを示しています。物流・倉庫事業者は、次に大きなユーザー層となると見込まれています。配送業務は労働集約的であり、かつサービス水準への要求も厳しいため、長時間を要するプログラミングの書き換えなしに、変動する業務量へ適応できるシステムへの需要が高まっているからです。

Robotics Foundation Models Market Analysis by 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. The robotics foundation models market is moving from laboratory work into commercial use in warehouses, factories, and healthcare facilities. Persistent shortages in manufacturing and logistics support demand for systems that can handle varied tasks with less programming. Firms are directing investment toward model platforms, data collection, simulation, and deployment tools rather than relying on a single robot design. Open models are widening access for smaller integrators, although they also reduce the ability to differentiate through model architecture alone. The robotics foundation models market still faces limits in real-world training data, safety validation, and liability rules, especially where robots work near people.

Key Report Takeaways

• By model architecture, Vision-Language-Action models held 54.67% revenue share in the robotics foundation models market in 2025, while embodied reasoning models are projected to expand at a 46.53% CAGR through 2031.
• By deployment mode, cloud-based deployment accounted for 57.26% of revenue in 2025 and is projected to expand at a 43.61% CAGR through 2031.
• By application, warehouse picking and sorting accounted for 23.16% of revenue in 2025, while home service execution is projected to expand at a 46.32% CAGR through 2031 in the robotics foundation models market.
• By end user, manufacturers held 35.21% revenue share in 2025, while healthcare providers are projected to expand at a 47.27% CAGR through 2031.
• By geography, North America held a 45.74% revenue share in the robotics foundation models market in 2025, while Asia-Pacific is projected to expand at a 46.84% CAGR through 2031.

Note: Market size and forecast figures in this report are generated using Mordor Intelligence’s proprietary estimation framework, updated with the latest available data and insights as of January 2026.

Global Robotics Foundation Models Market Trends and Insights

Drivers Impact Analysis*

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.[1] 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.[2] 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.

Expansion of Multimodal Vision-Language-Action Models

Vision-Language-Action, or VLA, models combine visual inputs, language instructions, and robot-state information within one control system. This can reduce the integration burden associated with separate perception, planning, and action components. NVIDIA stated that GR00T N1.7 was trained using 32,000 hours of real demonstration and egocentric human data, along with 8,000 hours of simulated rollouts. A common model can be post-trained for material handling, packaging, and inspection, reducing the number of configurations an enterprise needs to maintain. In healthcare, NVIDIA-Medtech reported that GR00T-H-N1.7 achieved 25% full end-to-end suturing success on the SutureBot benchmark. These developments broaden the robotics foundation models market beyond traditional industrial automation while raising the importance of safety assessment in settings where robot behavior affects workers, patients, and customers.

Open Robotics Models Lowering Development Barriers

Open-source and open-weight models are changing the cost structure of robot software development. OpenVLA used 970,000 robot episodes from the Open X-Embodiment dataset and outperformed RT-2-X by 16.5% in absolute task success across 29 evaluation tasks while using fewer parameters. This evidence indicates that robotics performance can improve through better training data and model design rather than parameter count alone. Xiaomi released Xiaomi-Robotics-0 with weights, inference code, and evaluation tools, then released post-training code for real-robot deployment. As access to baseline models improves, integrators can concentrate on deployment, data collection, and customer-specific validation. The robotics foundation models market may therefore see faster participation by mid-sized integrators, although model providers must protect performance advantages through data and operating experience accumulated across real customer environments and varied physical conditions.

Restraints Impact Analysis*

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.[3] 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.

Safety Certification and Liability Uncertainty

Safety rules for learning-based robot behavior have not progressed as quickly as model deployment. ISO 10218-1:2025 and ISO 10218-2:2025 set safety requirements for industrial robots, including requirements relevant to design and use. ANSI/A3 R15.06-2025 also updated industrial robot safety requirements for functional safety, risk assessment, end effectors, and cybersecurity. These standards are important, but do not establish complete evaluation methods for neural-network action policies that encounter unseen conditions. Responsibility may remain unclear among the model developer, robot manufacturer, system integrator, and operator if a robot causes damage or injury. This uncertainty can delay use in healthcare, defense, and public spaces, limiting near-term demand where operators need predictable evidence before allowing robots to work alongside people in the robotics foundation models market.

*Our forecasts treat driver/restraint impacts as directional, not additive. The impact forecasts reflect baseline growth, mix effects, and variable interactions.

Segment Analysis

By Model Architecture: VLA Models Lead While Reasoning Architectures Extend Capability

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.[4] 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.

By Deployment Mode: Cloud Systems Support Continuous Model Improvement

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.

By Application: Warehouse Use Leads While Home Service Use Accelerates

Warehouse picking and sorting held 23.16% of revenue in 2025. Warehouses require robots to handle varied stock-keeping units, irregular packages, mixed-weight totes, and changing storage patterns. These tasks are poorly suited to rigid automation because they require contextual recognition and flexible grasping. Foundation models can reduce manual reprogramming when packaging or product mixes change. The continued expansion of e-commerce and distribution operations gives the robotics foundation models market a dependable commercial base in warehouse automation.

Industrial assembly, material handling, packaging, and mobile inspection also remain meaningful application areas. Model-guided systems can adjust to new part shapes and production changes that previously required manual intervention. Mobile inspection and navigation can reduce human exposure in energy and manufacturing sites with hazardous conditions. Home service execution is projected to expand at a 46.32% CAGR through 2031, supported by lower humanoid hardware costs and stronger general-purpose model capability. Unstructured homes remain harder than factories because robots must work around vulnerable occupants, diverse objects, and limited machine-readable structure, so widespread use remains a longer-term commercial opportunity for the robotics foundation models market as reliability and safety expectations remain demanding.

By End User: Manufacturers Provide Volume While Healthcare Providers Advance Fastest

Manufacturers held 35.21% of revenue in 2025 and remained the largest end-user group. Automotive producers, electronics assemblers, and food processors need adaptable automation because product variation and shorter production runs make fixed programming less efficient. Foundation models offer greater flexibility, not just lower labor costs, across changing operating conditions. Commercial deployments of humanoid and general-purpose systems show that these technologies are moving beyond pilot settings in manufacturing and logistics. Logistics and warehousing providers represent the next major user group because distribution work is labor-intensive and service targets are strict, creating demand for systems that can adjust to changing workloads without lengthy reprogramming cycles.

Healthcare providers are projected to expand at a 47.27% CAGR through 2031, the fastest end-user rate. Surgical robotics and hospital logistics can extend clinical capacity and improve operational workflows where safety and validation requirements are met. NVIDIA-Medtech presented GR00T-H-N1.7 as a commercially licensed model for surgical and healthcare robotics using data from 50 or more institutions and 20 robot platforms. The company reported 25% full end-to-end suturing success on the SutureBot benchmark. Clinical validation and medical-device compliance will influence adoption timing, particularly in European surgical settings, where deployment must align with clinical validation and established medical-device processes 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, 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).[5] 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.

Competitive Landscape

The robotics foundation models market is moderately concentrated at the frontier model layer. A limited group of well-funded developers competes with specialist deployers, hardware manufacturers, and infrastructure providers. NVIDIA competes through a development stack that combines Isaac GR00T models, Isaac Lab, Isaac Sim, and Jetson hardware. NVIDIA also works with robot makers, including Unitree Robotics and Agile Robots, to support post-training and deployment. This strategy can strengthen NVIDIA’s position across the value chain as general-purpose robots enter more customer settings, including facilities that need linked tools for simulation, training, inference, and integration.

Data accumulation is likely to influence competitive positions as much as model architecture. Universal Robots and Scale AI established UR AI Trainer to collect data from production robots, creating a route from physical deployment to model improvement. Providers with operating fleets can collect manipulation, motion, and visual data under real-world conditions, creating an advantage that entrants cannot easily replicate at a comparable scale. Open systems such as OpenVLA and NVIDIA’s Apache-licensed GR00T model also provide integrators with capable starting points. This availability can pressure model-layer margins and shift value toward data, integration, safety validation, and customer support.

Physical Intelligence published π0.7 in April 2026 to demonstrate generalist manipulation across tasks not explicitly included in training. Xiaomi released Xiaomi-Robotics-0 and later opened its post-training code, widening developer access to real-time VLA capabilities. Boston Dynamics described its collaboration with Toyota Research Institute on Large Behavior Models for Atlas, showing that established robotics firms are also pursuing foundation-model approaches through research partnerships. These choices increase competitive intensity while rewarding companies with proven deployments, deployment data, and credible safety processes that can be demonstrated to customers operating in regulated or people-facing environments.

Recent Industry Developments

• July 2026: FedEx Corp. and Dexterity Inc. announced an expanded collaboration to scale Dexterity’s Foresight world model and Mech trailer-loading systems at the FedEx Hagerstown Hub in Maryland, a production-grade deployment of a physical AI world model in high-volume logistics, extending well beyond the initial pilot site.
• June 2026: NVIDIA released GR00T-H-N1.7, the first commercially licensed AI foundation model for surgical and healthcare robotics under the NVIDIA Open Model License, covering 20 robot platforms and 50 or more institutions. The model achieved 25% full end-to-end suturing success on the SutureBot benchmark, versus 0% for all prior models.
• April 2026: Physical Intelligence published research on π0.7, a 5-billion-parameter steerable generalist robotic foundation model on a Gemma3 backbone, demonstrating compositional generalization to manipulation tasks unseen during training, including an air-fryer cooking sequence assembled from unrelated prior training episodes.
• March 2026: NVIDIA released GR00T-H, the first open foundation VLA model for medical robotics, trained on data from 50 or more institutions and multiple surgical platforms, and achieving 64% average success across a 29-step ex-vivo suturing sequence.

List of Companies Covered in this Report:

• 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

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, 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

---

---